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An L, Liu H, Li M, Ma J, Zheng L, Zhou J, Zhang J, Yuan Y, Wu X. Unveiling the impact of harvest time on Dioscorea opposita Thunb. cv. Tiegun maturity by NMR-based metabolomics and LC-MS/MS analysis. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:6342-6349. [PMID: 38415792 DOI: 10.1002/jsfa.13418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 01/29/2024] [Accepted: 02/25/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Dioscorea opposita Thunb. cv. Tiegun maturity (DM) is an important factor influencing its quality. However, there are few studies on the impact of harvest time on its maturation. In the present study, a NMR-based metabolomics approach was applied to investigate the dynamic metabolic changes of D. opposita Thunb. cv. Tiegun at six different harvest stages: stage 1 (S1), stage 2 (S2), Stage 3 (S3), stage 4 (S4), stage 5 (S5) and stage 6 (S6). RESULTS Principal component analysis showed distinct segregation of samples obtained from S1, S2 and S3 compared to those derived from S4, S5 and S6. Interestingly, these samples from the two periods were obtained before and after frost, indicating that frost descent might be important for DM. Eight differential metabolites responsible for good separation of different groups were identified by the principal component analysis loading plot and partial least squares-discriminant analysis. In addition, quantitative analysis of these metabolites using liquid chromatography-tandem mass spectrometry determined the effects of harvest time on these metabolite contents, two of which, sucrose and allantoin, were considered as potential biomarkers to determine DM. CONCLUSION The present study demonstrated that NMR-based metabolomics approach could serve as a powerful tool to identify differential metabolites during harvesting processes, also offering a fresh insight into understanding the DM and the potential mechanism of quality formation. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Li An
- Institute of Quality and Safety for Agro-products, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Grain Quality and Safety and Testing Henan Province, Zhengzhou, China
| | - Hao Liu
- International Joint Research Laboratory for Global Change Ecology, School of Life Sciences, Henan University, Kaifeng, China
| | - Meng Li
- Institute of Quality and Safety for Agro-products, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Grain Quality and Safety and Testing Henan Province, Zhengzhou, China
| | - Jingwei Ma
- Institute of Quality and Safety for Agro-products, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Grain Quality and Safety and Testing Henan Province, Zhengzhou, China
| | - Lufei Zheng
- Institute of Quality Standards and Testing Technology for Agro-products of CAAS, Beijing, China
| | - Juan Zhou
- Institute of Quality and Safety for Agro-products, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Grain Quality and Safety and Testing Henan Province, Zhengzhou, China
| | - Junfeng Zhang
- Institute of Quality and Safety for Agro-products, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Grain Quality and Safety and Testing Henan Province, Zhengzhou, China
| | - Yongliang Yuan
- Department of Pharmacy, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xujin Wu
- Institute of Quality and Safety for Agro-products, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Grain Quality and Safety and Testing Henan Province, Zhengzhou, China
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350 10.1002/mrc.5350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/23/2024]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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Montoya-García CO, García-Mateos R, Magdaleno-Villar JJ, Volke-Haller VH, Villa-Ruano N, Zepeda-Vallejo LG, Becerra-Martínez E. NMR-based metabolomics to determine the fluctuation of metabolites in hydroponic purslane crops at different harvesting times. Food Res Int 2023; 166:112489. [PMID: 36914359 DOI: 10.1016/j.foodres.2023.112489] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 12/05/2022] [Accepted: 01/17/2023] [Indexed: 01/22/2023]
Abstract
Purslane (Portulaca oleracea L.) has a high content of nutrients and medicinal effects that depend on the genotype, harvesting time, and production system. The objective of the present research work was to elucidate the NMR-based metabolomics profiling of three native purslane cultivars from Mexico (Xochimilco, Mixquic, and Cuautla) grown under hydroponic conditions and harvested in three different times (32, 39, and 46 days after emergence). Thirty-nine metabolites identified in the 1H NMR spectra of aerial parts of purslane, 5 sugars, 15 amino acids, 8 organic acids, 3 caffeoylquinic acids, as well as 2 alcohols and 3 nucleosides, choline, O-phosphocholine and trigonelline were also detected. A total of 37 compounds were detected in native purslane from Xochimilco and Cuautla, whereas 39 compounds were detected in purslane from Mixquic. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) separated the cultivars into three clusters. Mixquic cultivar had the highest number of differential compounds (amino acids and carbohydrates), followed by Xochimilco and Cuautla cultivars, respectively. Changes in the metabolome were observed in latest times of harvest for all the cultivars studied. The differential compounds were glucose, fructose, galactose, pyruvate, choline, and 2-hydroxysobutyrate. The results obtained in this investigation may contribute to selecting the best cultivar of purslane and the best time in which the levels of nutrients are optimal.
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Affiliation(s)
- César Omar Montoya-García
- Universidad Autónoma Chapingo - Departamento de Fitotecnia, Km. 38.5, Carretera México-Texcoco, 56230 Chapingo, Estado de México, Mexico
| | - Rosario García-Mateos
- Universidad Autónoma Chapingo - Departamento de Fitotecnia, Km. 38.5, Carretera México-Texcoco, 56230 Chapingo, Estado de México, Mexico.
| | - J Jesús Magdaleno-Villar
- Universidad Autónoma Chapingo - Departamento de Fitotecnia, Km. 38.5, Carretera México-Texcoco, 56230 Chapingo, Estado de México, Mexico
| | - Víctor Hugo Volke-Haller
- Colegio de Postgraduados - Campus Montecillo, Km. 36.5, Carretera México-Texcoco, Montecillo, Texcoco, 56230 Estado de México, Mexico
| | - Nemesio Villa-Ruano
- CONACyT-Centro Universitario de Vinculación y Transferencia de Tecnología, Benemérita Universidad Autónoma de Puebla, CP 72570 Puebla, Mexico
| | - L Gerardo Zepeda-Vallejo
- Departamento de Química Orgánica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Prol. de Carpio y Plan de Ayala S/N, Col. Santo Tomas, Delegación, Miguel Hidalgo, Ciudad de México 11340, Mexico
| | - Elvia Becerra-Martínez
- Centro de Nanociencias y Micro y Nanotecnologías, Instituto Politécnico Nacional, Av. Luis Enrique Erro S/N, Unidad Profesional Adolfo López Mateos, Zacatenco, Delegación Gustavo A. Madero, Ciudad de México 07738, Mexico.
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de Medeiros LS, de Araújo Júnior MB, Peres EG, da Silva JCI, Bassicheto MC, Di Gioia G, Veiga TAM, Koolen HHF. Discovering New Natural Products Using Metabolomics-Based Approaches. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1439:185-224. [PMID: 37843810 DOI: 10.1007/978-3-031-41741-2_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.
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Affiliation(s)
- Lívia Soman de Medeiros
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil.
| | - Moysés B de Araújo Júnior
- Grupo de Pesquisa em Metabolômica e Espectrometria de Massas, Universidade do Estado do Amazonas, Manaus, Brazil
| | - Eldrinei G Peres
- Grupo de Pesquisa em Metabolômica e Espectrometria de Massas, Universidade do Estado do Amazonas, Manaus, Brazil
| | | | - Milena Costa Bassicheto
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil
| | - Giordanno Di Gioia
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil
| | - Thiago André Moura Veiga
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil
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Metabolomics Studies in Psoriatic Disease: A Review. Metabolites 2021; 11:metabo11060375. [PMID: 34200760 PMCID: PMC8230373 DOI: 10.3390/metabo11060375] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/03/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022] Open
Abstract
Metabolomics investigates a broad range of small molecules, allowing researchers to understand disease-related changes downstream of the genome and proteome in response to external environmental stimuli. It is an emerging technology that holds promise in identifying biomarkers and informing the practice of precision medicine. In this review, we summarize the studies that have examined endogenous metabolites in patients with psoriasis and/or psoriatic arthritis using nuclear magnetic resonance (NMR) or mass spectrometry (MS) and were published through 26 January 2021. A standardized protocol was used for extracting data from full-text articles identified by searching OVID Medline ALL, OVID Embase, OVID Cochrane Central Register of Controlled Trials and BIOSIS Citation Index in Web of Science. Thirty-two studies were identified, investigating various sample matrices and employing a wide variety of methods for each step of the metabolomics workflow. The vast majority of studies identified metabolites, mostly amino acids and lipids that may be associated with psoriasis diagnosis and activity. Further exploration is needed to identify and validate metabolomic biomarkers that can accurately and reliably predict which psoriasis patients will develop psoriatic arthritis, differentiate psoriatic arthritis patients from patients with other inflammatory arthritides and measure psoriatic arthritis activity.
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Abstract
This paper aims to cover the main strategies based on ion mobility spectrometry (IMS) for the analysis of biological samples. The determination of endogenous and exogenous compounds in such samples is important for the understanding of the health status of individuals. For this reason, the development of new approaches that can be complementary to the ones already established (mainly based on liquid chromatography coupled to mass spectrometry) is welcomed. In this regard, ion mobility spectrometry has appeared in the analytical scenario as a powerful technique for the separation and characterization of compounds based on their mobility. IMS has been used in several areas taking advantage of its orthogonality with other analytical separation techniques, such as liquid chromatography, gas chromatography, capillary electrophoresis, or supercritical fluid chromatography. Bioanalysis is not one of the areas where IMS has been more extensively applied. However, over the last years, the interest in using this approach for the analysis of biological samples has clearly increased. This paper introduces the reader to the principles controlling the separation in IMS and reviews recent applications using this technique in the field of bioanalysis.
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Pomyen Y, Wanichthanarak K, Poungsombat P, Fahrmann J, Grapov D, Khoomrung S. Deep metabolome: Applications of deep learning in metabolomics. Comput Struct Biotechnol J 2020; 18:2818-2825. [PMID: 33133423 PMCID: PMC7575644 DOI: 10.1016/j.csbj.2020.09.033] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/21/2020] [Accepted: 09/21/2020] [Indexed: 01/11/2023] Open
Abstract
In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.
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Key Words
- AI, Artificial Intelligence
- ANN, Artificial Neural Network
- AUC, Area Under the receiver-operating characteristic Curve
- Artificial neural network
- CCS value, Collision Cross Section value
- CFM-EI, Competitive Fragmentation Modeling-Electron Ionization
- CNN, Convolutional Neural Network
- DL, Deep Learning
- DNN, Deep Neural Network
- Deep learning
- ECFP, Extended Circular Fingerprint
- ER, Estrogen Receptor
- FID, Free Induction Decay
- FP score, Fingerprint correlation score
- FTIR, Fourier Transform Infrared
- GC–MS, Gas Chromatography-Mass Spectrometry
- HDLSS data, High Dimensional Low Sample Size data
- IST, Iterative Soft Thresholding
- LC-MS, Liquid Chromatography-Mass Spectrometry
- LSTM, Long Short-Term Memory
- ML, Machine Learning
- MLP, Multi-layered Perceptron
- MS, Mass Spectrometry
- Mass spectrometry
- Metabolomics
- NEIMS, Neural Electron-Ionization Mass Spectrometry
- NMR
- NMR, Nuclear Magnetic Resonance
- NUS, Non-Uniformly Sampling
- PARAFAC2, Parallel Factor Analysis 2
- RF, Random Forest
- RNN, Recurrent Neural Network
- ReLU, Rectified Linear Unit
- SMARTS, SMILES arbitrary target specification
- SMILE, Sparse Multidimensional Iterative Lineshape-enhanced
- SMILES, Simplified Molecular-Input Line-Entry System
- SRA, Sequence Read Archive
- VAE, Variational Autoencoder
- istHMS, Implementation of IST at Harvard Medical School
- m/z, mass/charge ratio
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Affiliation(s)
- Yotsawat Pomyen
- Translational Research Unit, Chulabhorn Research Institute, Bangkok, Thailand
| | - Kwanjeera Wanichthanarak
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Patcha Poungsombat
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand
| | - Johannes Fahrmann
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Dmitry Grapov
- CDS- Creative Data Solutions LLC, https://creative-data.solutions, USA
| | - Sakda Khoomrung
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand
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