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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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Dai H, Gao Q, Lu J, He L. Improving the Accuracy of Saffron Adulteration Classification and Quantification through Data Fusion of Thin-Layer Chromatography Imaging and Raman Spectral Analysis. Foods 2023; 12:2322. [PMID: 37372533 DOI: 10.3390/foods12122322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/02/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Agricultural crops of high value are frequently targeted by economic adulteration across the world. Saffron powder, being one of the most expensive spices and colorants on the market, is particularly vulnerable to adulteration with extraneous plant materials or synthetic colorants. However, the current international standard method has several drawbacks, such as being vulnerable to yellow artificial colorant adulteration and requiring tedious laboratory measuring procedures. To address these challenges, we previously developed a portable and versatile method for determining saffron quality using a thin-layer chromatography technique coupled with Raman spectroscopy (TLC-Raman). In this study, our aim was to improve the accuracy of the classification and quantification of adulterants in saffron by utilizing mid-level data fusion of TLC imaging and Raman spectral data. In summary, the featured imaging data and featured Raman data were concatenated into one data matrix. The classification and quantification results of saffron adulterants were compared between the fused data and the analysis based on each individual dataset. The best classification result was obtained from the partial least squares-discriminant analysis (PLS-DA) model developed using the mid-level fusion dataset, which accurately determined saffron with artificial adulterants (red 40 or yellow 5 at 2-10%, w/w) and natural plant adulterants (safflower and turmeric at 20-100%, w/w) with an overall accuracy of 99.52% and 99.20% in the training and validation group, respectively. Regarding quantification analysis, the PLS models built with the fused data block demonstrated improved quantification performance in terms of R2 and root-mean-square errors for most of the PLS models. In conclusion, the present study highlighted the significant potential of fusing TLC imaging data and Raman spectral data to improve saffron classification and quantification accuracy via the mid-level data fusion, which will facilitate rapid and accurate decision-making on site.
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Affiliation(s)
- Haochen Dai
- Chenoweth Laboratory, Department of Food Science, University of Massachusetts Amherst, 102 Holdsworth Way, Amherst, MA 01003, USA
| | - Qixiang Gao
- Chenoweth Laboratory, Department of Food Science, University of Massachusetts Amherst, 102 Holdsworth Way, Amherst, MA 01003, USA
| | - Jiakai Lu
- Chenoweth Laboratory, Department of Food Science, University of Massachusetts Amherst, 102 Holdsworth Way, Amherst, MA 01003, USA
| | - Lili He
- Chenoweth Laboratory, Department of Food Science, University of Massachusetts Amherst, 102 Holdsworth Way, Amherst, MA 01003, USA
- Department of Chemistry, University of Massachusetts, Amherst, MA 01002, USA
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Wang H, Xiao H, Wu Y, Zhou F, Hua C, Ba L, Shamim S, Zhang W. Characterization of volatile compounds and microstructure in different tissues of ‘Eureka’ lemon ( Citrus limon). INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2022. [DOI: 10.1080/10942912.2022.2046600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Haiou Wang
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing Research Institute for Agricultural Mechanization, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
| | - HongWei Xiao
- College of Engineering, China Agricultural University, Beijing, PR China
| | - Yulong Wu
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
| | - Feng Zhou
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
| | - Chun Hua
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
| | - Long Ba
- State Laboratory of Bioelectronics, School of Biomedical Engineering and Department of Physics, Southeast University, Nanjing, PR China
| | - Sara Shamim
- State Laboratory of Bioelectronics, School of Biomedical Engineering and Department of Physics, Southeast University, Nanjing, PR China
| | - Wei Zhang
- School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
- Jiangsu Provincial Key Construction Laboratory of Special Biomass Waste Resource Utilization, School of Food Science, Nanjing Xiaozhuang University, Nanjing, PR China
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Statistical Integration of 'Omics Data Increases Biological Knowledge Extracted from Metabolomics Data: Application to Intestinal Exposure to the Mycotoxin Deoxynivalenol. Metabolites 2021; 11:metabo11060407. [PMID: 34205708 PMCID: PMC8233929 DOI: 10.3390/metabo11060407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/07/2021] [Accepted: 06/15/2021] [Indexed: 12/18/2022] Open
Abstract
The effects of low doses of toxicants are often subtle and information extracted from metabolomic data alone may not always be sufficient. As end products of enzymatic reactions, metabolites represent the final phenotypic expression of an organism and can also reflect gene expression changes caused by this exposure. Therefore, the integration of metabolomic and transcriptomic data could improve the extracted biological knowledge on these toxicants induced disruptions. In the present study, we applied statistical integration tools to metabolomic and transcriptomic data obtained from jejunal explants of pigs exposed to the food contaminant, deoxynivalenol (DON). Canonical correlation analysis (CCA) and self-organizing map (SOM) were compared for the identification of correlated transcriptomic and metabolomic features, and O2-PLS was used to model the relationship between exposure and selected features. The integration of both 'omics data increased the number of discriminant metabolites discovered (39) by about 10 times compared to the analysis of the metabolomic dataset alone (3). Besides the disturbance of energy metabolism previously reported, assessing correlations between both functional levels revealed several other types of damage linked to the intestinal exposure to DON, including the alteration of protein synthesis, oxidative stress, and inflammasome activation. This confirms the added value of integration to enrich the biological knowledge extracted from metabolomics.
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Russo M, Rigano F, Arigò A, Dugo P, Mondello L. Coumarins, Psoralens and Polymethoxyflavones in Cold-pressed Citrus Essential Oils: a Review. JOURNAL OF ESSENTIAL OIL RESEARCH 2020. [DOI: 10.1080/10412905.2020.1857855] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Marina Russo
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
| | - Francesca Rigano
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
| | - Adriana Arigò
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
| | - Paola Dugo
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
| | - Luigi Mondello
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Messina, Italy
- Department of Sciences and Technologies for Human and Environment, University Campus Bio-Medico of Rome, Rome, Italy
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Letertre MPM, Dervilly G, Giraudeau P. Combined Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry Approaches for Metabolomics. Anal Chem 2020; 93:500-518. [PMID: 33155816 DOI: 10.1021/acs.analchem.0c04371] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Marie B. Disentangling of the ecotoxicological signal using "omics" analyses, a lesson from the survey of the impact of cyanobacterial proliferations on fishes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 736:139701. [PMID: 32497891 DOI: 10.1016/j.scitotenv.2020.139701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/16/2020] [Accepted: 05/23/2020] [Indexed: 06/11/2023]
Abstract
Omics technologies offer unprecedented perspectives for the rational investigation of complex biological systems. Indeed, omics present the ability of offering an extensive perception of the biochemistry and physiology of the cell and of any perturbing consequences of contaminants through the joint investigation of thousands of molecular responses simultaneously; then it has recently conducted to a fervent attention by research ecotoxicologists. Beyond the presentation of latest advances, exemplified here by omics investigation of cyanobacterial deleterious effects on various fishes (at various experimental and biological scales and with various analytical tools and pipeline), the present review paper re-explores the promising perspectives and also the pitfalls of such holistic investigations of the ecotoxicological response of organisms for environmental assessment.
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Affiliation(s)
- Benjamin Marie
- Muséum National d'Histoire Naturelle, UMR 7245, CNRS, MNHN Molécules de Communication et Adaptation des Micro-organismes (MCAM), équipe "Cyanobactéries, Cyanotoxines et Environnement", 12 rue Buffon, CP 39, 75231 Paris Cedex 05, France.
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Chemometric Strategies for Spectroscopy-Based Food Authentication. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186544] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description.
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Palaric C, Pilard S, Fontaine JX, Boccard J, Mathiron D, Rigaud S, Cailleu D, Mesnard F, Gut Y, Renaud T, Petit A, Beaumal JY, Molinié R. Processing of NMR and MS metabolomics data using chemometrics methods: a global tool for fungi biotransformation reactions monitoring. Metabolomics 2019; 15:107. [PMID: 31346787 DOI: 10.1007/s11306-019-1567-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 07/16/2019] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Biotransformation constitutes an important aspect of the drug discovery process, to mimic human metabolism of active principal ingredient but also to generate new chemical entities. Several microorganisms such as fungi are well adapted to transform drug, whether at the stage of screening or for large-scale production. OBJECTIVES Due to the high chemical complexity of the biotransformation media, it seems attractive to develop new analytical strategies in order to guarantee an adequate monitoring and optimize the production of targeted metabolites or drug candidates. METHODS The model designed for this purpose concerns the biotransformation of a potential histamine H3 antagonist (S38093) in order to produce phase I metabolites. MS, NMR and chemometrics tools were used to monitor biotransformation reactions. RESULTS First, a screening of eleven filamentous fungi was carried out by UHPLC-UV-MS and principal component analysis to select the best candidates. Subsequently, MS (tR, m/z) and NMR (1H, JRES) fingerprints associated with Consensus OPLS-DA multiblock approach were used to better understand the bioreaction mechanisms in terms of nutrient consumption and hydroxylated metabolites production. Then an experimental design was set up to optimize the production conditions (pH, kinetic) of these target metabolites. CONCLUSION This study demonstrates how NMR and MS acquisitions combined with chemometric methods offer an innovative analytical strategy to have a grasp of functionalization mechanisms, and identify metabolites and other compounds (amino acids, nutrients, etc.) in complex biotransformation mixtures.
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Affiliation(s)
- Cécile Palaric
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France
- BIOPI EA 3900, Biologie des Plantes et Innovation, Université de Picardie Jules Verne, 1 rue des Louvels, 80000, Amiens, France
- Technologie Servier, 27 rue Eugène Vignat, 45000, Orléans, France
| | - Serge Pilard
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France.
| | - Jean-Xavier Fontaine
- BIOPI EA 3900, Biologie des Plantes et Innovation, Université de Picardie Jules Verne, 1 rue des Louvels, 80000, Amiens, France
| | - Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1211, Geneva 4, Switzerland
| | - David Mathiron
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France
| | - Sébastien Rigaud
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France
| | - Dominique Cailleu
- Plateforme-analytique, Institut de Chimie de Picardie FR CNRS 3085, Université de Picardie Jules Verne, 33 rue Saint Leu, 80039, Amiens, France
| | - François Mesnard
- BIOPI EA 3900, Biologie des Plantes et Innovation, Université de Picardie Jules Verne, 1 rue des Louvels, 80000, Amiens, France
| | - Yoann Gut
- Technologie Servier, 27 rue Eugène Vignat, 45000, Orléans, France
| | - Tristan Renaud
- Technologie Servier, 27 rue Eugène Vignat, 45000, Orléans, France
| | - Alain Petit
- Technologie Servier, 27 rue Eugène Vignat, 45000, Orléans, France
| | | | - Roland Molinié
- BIOPI EA 3900, Biologie des Plantes et Innovation, Université de Picardie Jules Verne, 1 rue des Louvels, 80000, Amiens, France
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Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage. J Pharm Biomed Anal 2018; 161:313-325. [PMID: 30195171 DOI: 10.1016/j.jpba.2018.08.046] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 08/22/2018] [Accepted: 08/23/2018] [Indexed: 12/16/2022]
Abstract
Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.
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Griffith CM, Morgan MA, Dinges MM, Mathon C, Larive CK. Metabolic Profiling of Chloroacetanilide Herbicides in Earthworm Coelomic Fluid Using 1H NMR and GC-MS. J Proteome Res 2018; 17:2611-2622. [PMID: 29939029 DOI: 10.1021/acs.jproteome.8b00081] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Earthworms ( Eisenia fetida) are vital members of the soil environment. Because of their sensitivity to many contaminants, monitoring earthworm metabolism may be a useful indicator of environmental stressors. Here, metabolic profiles of exposure to five chloroacetanilide herbicides and one enantiomer (acetochlor, alachlor, butachlor, racemic metolachlor, S-metolachlor, and propachlor) are observed in earthworm coelomic fluid using proton nuclear magnetic resonance spectroscopy (NMR) and gas chromatography-mass spectrometry (GC-MS). Multiblocked-orthogonal partial least-squares-discriminant analysis (MB-OPLS-DA) and univariate analysis were used to identify metabolic perturbations in carnitine biosynthesis, carbohydrate metabolism, lipid metabolism, nitrogen metabolism, and the tricarboxylic acid cycle. Intriguingly, stereospecific metabolic responses were observed between racemic metolachlor and S-metolachlor exposed worms. These findings support the utility of coelomic fluid in monitoring metabolic perturbations induced by chloroacetanilide herbicides in nontarget organisms and reveal specificity in the metabolic impacts of herbicide analogues in earthworms.
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