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Nordin N, Hasbullah NH, Abu Bakar N. Comprehensive analysis of alcohol compounds in commercial instant coffee: A validated 1H NMR spectroscopy study within the Islamic paradigm. Food Chem 2024; 458:140236. [PMID: 38959805 DOI: 10.1016/j.foodchem.2024.140236] [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: 02/10/2024] [Revised: 06/13/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
Coffee, a globally consumed beverage, has raised concerns in Islamic jurisprudence due to the possible presence of alcohol compounds. This research aims to utilise the sensitivity and reliability of 1H NMR spectroscopy in the quantification of alcohol compounds such as ethanol, furfuryl alcohol, and 5-(hydroxymethyl) furfural (HMF) in commercial instant coffee. Analysis of seven products was performed using advanced 1H Nuclear Magnetic Resonance (NMR) spectroscopy together with Statistical Total Correlation Spectroscopy (STOCSY) and Resolution-Enhanced (RED)-STORM. The analysis of the 100 mg sample revealed the absence of ethanol. The amount of furfuryl alcohol and HMF in the selected commercial instant coffee samples was 0.817 μg and 0.0553 μg, respectively. This study demonstrates the utility of 1H NMR spectroscopy in accurate quantification of trace components for various applications.
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Affiliation(s)
- Nurdiana Nordin
- Department of Chemistry, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Nurul Helwani Hasbullah
- Department of Chemistry, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Nurfarhanim Abu Bakar
- Department of Engineering and Sciences, American Degree Program, School of Liberal Arts and Sciences, Taylor's University, Taylor's Lakeside Campus, No. 1 Jalan Taylor, 47500 Subang Jaya, Selangor, Malaysia.
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2
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Ferreira H, Duarte D, Carneiro TJ, Costa C, Barbosa JC, Rodrigues JE, Alves P, Vasconcelos M, Pinto E, Gomes A, Gil AM. Impact of a legumes diet on the human gut microbiome articulated with fecal and plasma metabolomes: A pilot study. Clin Nutr ESPEN 2024; 63:332-345. [PMID: 38964655 DOI: 10.1016/j.clnesp.2024.06.051] [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: 04/18/2024] [Revised: 06/12/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND & AIMS Legumes intake is known to be associated with several health benefits the origins of which is still a matter of debate. This paper addresses a pilot small cohort to probe for metabolic aspects of the interplay between legumes intake, human metabolism and gut microbiota. METHODS Untargeted nuclear magnetic resonance (NMR) metabolomics of blood plasma and fecal extracts was carried out, in tandem with qPCR analysis of feces, to assess the impact of an 8-week pilot legumes diet intervention on the fecal and plasma metabolomes and gut microbiota of 19 subjects. RESULTS While the high inter-individual variability hindered the detection of statistically significant changes in the gut microbiome, increased fecal glucose and decreased threonine levels were noted. Correlation analysis between the microbiome and fecal metabolome lead to putative hypotheses regarding the metabolic activities of prevalent bacteria groups (Clostridium leptum subgroup, Roseburia spp., and Faecalibacterium prausnitzii). These included elevated fecal glucose as a preferential energy source, the involvement of valerate/isovalerate and reduced protein degradation in gut microbiota. Plasma metabolomics advanced mannose and betaine as potential markers of legume intake and unveiled a decrease in formate and ketone bodies, the latter suggesting improved energy utilization through legume carbohydrates. Amino acid metabolism was also apparently affected, as suggested by lowered urea, histidine and threonine levels. CONCLUSIONS Despite the high inter-individual gut microbiome variability characterizing the small cohort addressed, combination of microbiological measurements and untargeted metabolomics unveiled several metabolic effects putatively related to legumes intake. If confirmed in larger cohorts, our findings will support the inclusion of legumes in diets and contribute valuable new insight into the origins of associated health benefits.
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Affiliation(s)
- Helena Ferreira
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal; Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
| | - Daniela Duarte
- Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
| | - Tatiana J Carneiro
- Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
| | - Célia Costa
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - Joana C Barbosa
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - João E Rodrigues
- Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
| | - Paulo Alves
- Universidade Católica Portuguesa, CIIS - Centro de Investigação Interdisciplinar em Saúde, Escola Enfermagem (Porto), Portugal
| | - Marta Vasconcelos
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - Elisabete Pinto
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal; EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
| | - Ana Gomes
- Universidade Católica Portuguesa, CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - Ana M Gil
- Department of Chemistry and CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal.
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Rowley CE, Lodge S, Egan S, Itsiopoulos C, Christophersen CT, Silva D, Kicic-Starcevich E, O’Sullivan TA, Wist J, Nicholson J, Frost G, Holmes E, D’Vaz N. Altered dietary behaviour during pregnancy impacts systemic metabolic phenotypes. Front Nutr 2023; 10:1230480. [PMID: 38111603 PMCID: PMC10725961 DOI: 10.3389/fnut.2023.1230480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 10/25/2023] [Indexed: 12/20/2023] Open
Abstract
Rationale Evidence suggests consumption of a Mediterranean diet (MD) can positively impact both maternal and offspring health, potentially mediated by a beneficial effect on inflammatory pathways. We aimed to apply metabolic profiling of serum and urine samples to assess differences between women who were stratified into high and low alignment to a MD throughout pregnancy and investigate the relationship of the diet to inflammatory markers. Methods From the ORIGINS cohort, 51 pregnant women were stratified for persistent high and low alignment to a MD, based on validated MD questionnaires. 1H Nuclear Magnetic Resonance (NMR) spectroscopy was used to investigate the urine and serum metabolite profiles of these women at 36 weeks of pregnancy. The relationship between diet, metabolite profile and inflammatory status was investigated. Results There were clear differences in both the food choice and metabolic profiles of women who self-reported concordance to a high (HMDA) and low (LMDA) Mediterranean diet, indicating that alignment with the MD was associated with a specific metabolic phenotype during pregnancy. Reduced meat intake and higher vegetable intake in the HMDA group was supported by increased levels of urinary hippurate (p = 0.044) and lower creatine (p = 0.047) levels. Serum concentrations of the NMR spectroscopic inflammatory biomarkers GlycA (p = 0.020) and GlycB (p = 0.016) were significantly lower in the HDMA group and were negatively associated with serum acetate, histidine and isoleucine (p < 0.05) suggesting a greater level of plant-based nutrients in the diet. Serum branched chain and aromatic amino acids were positively associated with the HMDA group while both urinary and serum creatine, urine creatinine and dimethylamine were positively associated with the LMDA group. Conclusion Metabolic phenotypes of pregnant women who had a high alignment with the MD were significantly different from pregnant women who had a poor alignment with the MD. The metabolite profiles aligned with reported food intake. Differences were most significant biomarkers of systemic inflammation and selected gut-microbial metabolites. This research expands our understanding of the mechanisms driving health outcomes during the perinatal period and provides additional biomarkers for investigation in pregnant women to assess potential health risks.
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Affiliation(s)
- Charlotte E. Rowley
- Australian National Phenome Centre, and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA, Australia
| | - Samantha Lodge
- Australian National Phenome Centre, and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA, Australia
| | - Siobhon Egan
- Australian National Phenome Centre, and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA, Australia
| | | | - Claus T. Christophersen
- WA Human Microbiome Collaboration Centre, Curtin University, Bentley, WA, Australia
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Desiree Silva
- Telethon Kids Institute, Perth Children’s Hospital, Nedlands, WA, Australia
- Joondalup Health Campus, Joondalup, WA, Australia
| | | | - Therese A. O’Sullivan
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Julien Wist
- Australian National Phenome Centre, and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA, Australia
- Chemistry Department, Universidad del Valle, Cali, Colombia
| | - Jeremy Nicholson
- Australian National Phenome Centre, and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA, Australia
- Faculty of Medicine, Imperial College London, Institute of Global Health Innovation, London, United Kingdom
- Section of Nutrition Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Gary Frost
- Australian National Phenome Centre, and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA, Australia
- Section of Nutrition Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Elaine Holmes
- Australian National Phenome Centre, and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA, Australia
- Faculty of Medicine, Imperial College London, Institute of Global Health Innovation, London, United Kingdom
- Section of Nutrition Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Nina D’Vaz
- Telethon Kids Institute, Perth Children’s Hospital, Nedlands, WA, Australia
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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.
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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.
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5
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Han X, Wang W, Ma LH, AI-Ramahi I, Botas J, MacKenzie K, Allen GI, Young DW, Liu Z, Maletic-Savatic M. SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra. Bioinformatics 2023; 39:btad593. [PMID: 37792497 PMCID: PMC10568371 DOI: 10.1093/bioinformatics/btad593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 07/31/2023] [Accepted: 10/02/2023] [Indexed: 10/06/2023] Open
Abstract
MOTIVATION Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy. RESULTS As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY's efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7 min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making. AVAILABILITY AND IMPLEMENTATION The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.
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Affiliation(s)
- Xu Han
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Wanli Wang
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Graduate Program of Quantitative & Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, United States
| | - Li-Hua Ma
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX 77030, United States
| | - Ismael AI-Ramahi
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States
| | - Juan Botas
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States
| | - Kevin MacKenzie
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX 77030, United States
- Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, United States
| | - Genevera I Allen
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Electrical and Computer Engineering, Statistics, and Computer Science, Rice University, Houston, TX 77005-1827, United States
| | - Damian W Young
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, United States
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Mirjana Maletic-Savatic
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX 77030, United States
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX 77030, United States
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6
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Han X, Wang W, Ma LH, Al-Ramahi I, Botas J, MacKenzie K, Allen GI, Young DW, Liu Z, Maletic-Savatic M. SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529564. [PMID: 36865102 PMCID: PMC9980041 DOI: 10.1101/2023.02.22.529564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.
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Affiliation(s)
- Xu Han
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Wanli Wang
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Graduate Program of Quantitative & Computational Biosciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Li-Hua Ma
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Ismael Al-Ramahi
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Juan Botas
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kevin MacKenzie
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX, 77030, USA
- Center for Drug Discovery, Department of Pathology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Genevera I. Allen
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Statistics, Rice University, 6100 Main Street, Houston, TX 77005-1827, USA
| | - Damian W. Young
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Mirjana Maletic-Savatic
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, 77030, USA
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Abstract
During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.
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Affiliation(s)
- Sofia Moco
- Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Amsterdam Institute for Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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8
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Yuan L, Muli S, Huybrechts I, Nöthlings U, Ahrens W, Scalbert A, Floegel A. Assessment of Fruit and Vegetables Intake with Biomarkers in Children and Adolescents and Their Level of Validation: A Systematic Review. Metabolites 2022; 12:126. [PMID: 35208201 PMCID: PMC8876138 DOI: 10.3390/metabo12020126] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/23/2022] [Accepted: 01/23/2022] [Indexed: 12/10/2022] Open
Abstract
Fruit and vegetables (FV) are part of a healthy diet and should be frequently consumed already at a young age. However, intake of FV is difficult to assess in children and adolescents due to various misreporting aspects. Thus, measurement of dietary biomarkers may be a promising alternative to assess FV intake more objectively at young age. To date, dietary biomarkers have been primarily studied in adults, and research focused on their usefulness in children is scarce. However, clinical studies have revealed important differences between children and adults, most importantly in their gut microbiome composition, resulting in differences in postprandial metabolism, as well as in food choices and meal compositions that may influence individual biomarker levels. Therefore, the present review aimed to identify biomarkers of FV intake (BFVI) currently available in children and adolescents and to explore whether there are any differences in the BFVI profile above between children and adolescents and adults. In addition, the current level of validation of BFVI in children and adolescents was examined. In total, 28 studies were eligible for this review, and 18 compounds were identified as potential biomarkers for FV intake in children and adolescents. Carotenoid concentration in skin was a valuable biomarker for total FV intake for both children and adult populations. Common BFVI in blood in adults (e.g., carotenoids and vitamin C) showed inconsistent results in children and adolescents. Biomarkers particularly useful in children included urinary hippuric acid as a biomarker of polyphenolic compound intake originating from FV and the combination of N-methylnicotinic acid and acetylornithine as a biomarker of bean intake. Further studies are needed to assess their kinetics, dose-response, and other validation aspects. There is limited evidence so far regarding valid BFVI in children and adolescents. Thus, to put BFVI into practice in children and adolescents, further studies, particularly based on metabolomics, are needed to identify and validate BFVI that can be used in future epidemiological studies.
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Affiliation(s)
- Li Yuan
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology—BIPS, Achterstraße 30, 28359 Bremen, Germany; (W.A.); (A.F.)
| | - Samuel Muli
- Unit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115 Bonn, Germany; (S.M.); (U.N.)
| | - Inge Huybrechts
- International Agency for Research on Cancer (IARC), 69372 Lyon, France; (I.H.); (A.S.)
| | - Ute Nöthlings
- Unit of Nutritional Epidemiology, Department of Nutrition and Food Sciences, Rheinische Friedrich-Wilhelms-University Bonn, 53115 Bonn, Germany; (S.M.); (U.N.)
| | - Wolfgang Ahrens
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology—BIPS, Achterstraße 30, 28359 Bremen, Germany; (W.A.); (A.F.)
| | - Augustin Scalbert
- International Agency for Research on Cancer (IARC), 69372 Lyon, France; (I.H.); (A.S.)
| | - Anna Floegel
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology—BIPS, Achterstraße 30, 28359 Bremen, Germany; (W.A.); (A.F.)
- Section of Dietetics, Faculty of Agriculture and Food Sciences, Hochschule Neubrandenburg—University of Applied Sciences, 17033 Neubrandenburg, Germany
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9
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Mujagic Z, Kasapi M, Jonkers DMAE, Garcia-Perez I, Vork L, Weerts ZZR, Serrano-Contreras JI, Zhernakova A, Kurilshikov A, Scotcher J, Holmes E, Wijmenga C, Keszthelyi D, Nicholson JK, Posma JM, Masclee AAM. Integrated fecal microbiome-metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome. Gut Microbes 2022; 14:2063016. [PMID: 35446234 PMCID: PMC9037519 DOI: 10.1080/19490976.2022.2063016] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS), several modalities of biological and clinical data must be combined. We aimed to identify profiles of fecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Fecal metabolites were measured using proton nuclear magnetic resonance (1H-NMR) spectroscopy and gut microbiome using shotgun metagenomic sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HCs) with extensive clinical, biological and phenotype information. Data were analyzed using support vector classification and regression and kernel t-SNE. Microbiome and metabolome profiles could distinguish IBS and HC with an area-under-the-receiver-operator-curve of 77.3% and 79.5%, respectively, but this could be improved by combining microbiota and metabolites to 83.6%. No significant differences in predictive ability of the microbiome-metabolome data were observed between the three classical, stool pattern-based, IBS subtypes. However, unsupervised clustering showed distinct subsets of IBS patients based on fecal microbiome-metabolome data. These clusters could be related plasma levels of serotonin and its metabolite 5-hydroxyindoleacetate, effects of psychological stress on gastrointestinal (GI) symptoms, onset of IBS after stressful events, medical history of previous abdominal surgery, dietary caloric intake and IBS symptom duration. Furthermore, pathways in metabolic reaction networks were integrated with microbiota data, that reflect the host-microbiome interactions in IBS. The identified microbiome-metabolome signatures for IBS, associated with altered serotonin metabolism and unfavorable stress response related to GI symptoms, support the microbiota-gut-brain link in the pathogenesis of IBS.
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Affiliation(s)
- Zlatan Mujagic
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands,Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK,CONTACT Zlatan Mujagic Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Melpomeni Kasapi
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK
| | - Daisy MAE Jonkers
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Isabel Garcia-Perez
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, London, UK
| | - Lisa Vork
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Zsa Zsa R.M. Weerts
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Jose Ivan Serrano-Contreras
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK
| | - Alexandra Zhernakova
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Alexander Kurilshikov
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jamie Scotcher
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK
| | - Elaine Holmes
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, London, UK,The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, Perth, Australia
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Daniel Keszthelyi
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Jeremy K Nicholson
- The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, Perth, Australia
| | - Joram M Posma
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK
| | - Ad AM Masclee
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
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10
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Smith CE, Parnell LD, Lai CQ, Rush JE, Freeman LM. Investigation of diets associated with dilated cardiomyopathy in dogs using foodomics analysis. Sci Rep 2021; 11:15881. [PMID: 34354102 PMCID: PMC8342479 DOI: 10.1038/s41598-021-94464-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 07/08/2021] [Indexed: 02/07/2023] Open
Abstract
Dilated cardiomyopathy (DCM) is a disease of the heart muscle that affects both humans and dogs. Certain canine diets have been associated with DCM, but the diet-disease link is unexplained, and novel methods are needed to elucidate mechanisms. We conducted metabolomic profiling of 9 diets associated with canine DCM, containing ≥ 3 pulses, potatoes, or sweet potatoes as main ingredients, and in the top 16 dog diet brands most frequently associated with canine DCM cases reported to the FDA (3P/FDA diets), and 9 non-3P/FDA diets. We identified 88 named biochemical compounds that were higher in 3P/FDA diets and 23 named compounds that were lower in 3P/FDA diets. Amino acids, amino acid-derived compounds, and xenobiotics/plant compounds were the largest categories of biochemicals that were higher in 3P/FDA diets. Random forest analyses identified the top 30 compounds that distinguished the two diet groups with 100% predictive accuracy. Four diet ingredients distinguished the two diet groups (peas, lentils, chicken/turkey, and rice). Of these ingredients, peas showed the greatest association with higher concentrations of compounds in 3P/FDA diets. Moreover, the current foodomics analyses highlight relationships between diet and DCM in dogs that can identify possible etiologies for understanding diet-disease relationships in dogs and humans.
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Affiliation(s)
- Caren E Smith
- Nutrition and Genomics Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Laurence D Parnell
- USDA Agricultural Research Service, Nutrition and Genomics Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - Chao-Qiang Lai
- USDA Agricultural Research Service, Nutrition and Genomics Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA
| | - John E Rush
- Department of Clinical Sciences, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA, USA
| | - Lisa M Freeman
- Department of Clinical Sciences, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA, USA.
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11
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Owen EJ, Patel S, Flannery O, Dew TP, O'Connor LM. Derivation and Validation of a Total Fruit and Vegetable Intake Prediction Model to Identify Targets for Biomarker Discovery Using the UK National Diet and Nutrition Survey. J Nutr 2021; 151:962-969. [PMID: 33484153 DOI: 10.1093/jn/nxaa406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/21/2020] [Accepted: 11/23/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Dietary assessments in research and clinical settings are largely reliant on self-reported questionnaires. It is acknowledged that these are subject to measurement error and biases and that objective approaches would be beneficial. Dietary biomarkers have been purported as a complementary approach to improve the accuracy of dietary assessments. Tentative biomarkers have been identified for many individual fruits and vegetables (FVs), but an objective total FV intake assessment tool has not been established. OBJECTIVES To derive and validate a prediction model of total FV intake (TFVpred) to inform future biomarker studies. METHODS Data from the National Diet and Nutrition Survey (NDNS) were used for this analysis. A modeling group (MG) consisting of participants aged >11 years from the NDNS years 5-6 was created (n = 1746). Intake data for 96 FVs were analyzed by stepwise regression to derive a model that satisfied 3 selection criteria: SEE ≤80, R2 >0.7, and ≤10 predictors. The TFVpred model was validated using comparative data from a validation group (VG) created from the NDNS years 7-8 (n = 1865). Pearson's correlation coefficients were assessed between observed and predicted values in the MG and VG. Bland-Altman plots were used to assess agreement between TFVpred estimates and total FV intake. RESULTS A TFVpred model, comprised of tomatoes, apples, carrots, bananas, pears, strawberries, and onions, satisfied the selection criteria (R2 = 0.761; SEE = 78.81). Observed and predicted total FV intake values were positively correlated in the MG (r = 0.872; P < 0.001; R2 = 0.761) and the VG (r = 0.838; P < 0.001; R2 = 0.702). In the MG and VG, 95.0% and 94.9%, respectively, of TFVpred model residuals were within the limits of agreement. CONCLUSIONS Intakes of a concise FV list can be used to predict total FV intakes in a UK population. The individual FVs included in the TFVpred model present targets for biomarker discovery aimed at objectively assessing total FV intake.
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Affiliation(s)
- Elliot J Owen
- Department of Health Professions, Faculty of Health, Psychology and Social Care, Manchester Metropolitan University, Manchester, United Kingdom.,Future Food Beacon of Excellence, University of Nottingham, Sutton Bonington, United Kingdom
| | - Sumaiya Patel
- Department of Health Professions, Faculty of Health, Psychology and Social Care, Manchester Metropolitan University, Manchester, United Kingdom
| | - Orla Flannery
- Department of Health Professions, Faculty of Health, Psychology and Social Care, Manchester Metropolitan University, Manchester, United Kingdom
| | - Tristan P Dew
- Department of Health Professions, Faculty of Health, Psychology and Social Care, Manchester Metropolitan University, Manchester, United Kingdom.,Future Food Beacon of Excellence, University of Nottingham, Sutton Bonington, United Kingdom.,School of Biosciences, University of Nottingham, Sutton Bonington, United Kingdom
| | - Laura M O'Connor
- Department of Health Professions, Faculty of Health, Psychology and Social Care, Manchester Metropolitan University, Manchester, United Kingdom
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12
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NMR Characterization of Ten Apple Cultivars from the Piedmont Region. Foods 2021; 10:foods10020289. [PMID: 33535442 PMCID: PMC7912530 DOI: 10.3390/foods10020289] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/18/2021] [Accepted: 01/25/2021] [Indexed: 12/28/2022] Open
Abstract
The metabolite profile of ten traditional apple cultivars grown in the Piedmont region (Italy) was studied by means of nuclear magnetic resonance spectroscopy, identifying an overall number of 36 compounds. A more complete assignment of the proton nuclear magnetic resonance (1H NMR) resonances from hydroalcoholic and organic apple extracts with respect to literature data was reported, identifying fructose tautomeric forms, galacturonic acid, γ-aminobutyric acid (GABA), p-coumaroyl moiety, phosphatidylcholine, and digalactosyldiacylglycerol. The chemical profile of each apple cultivar was defined by thorough quantitative NMR analysis of four sugars (fructose, glucose, sucrose, and xylose), nine organic acids (acetic, citric, formic, citramalic, lactic, malic, quinic, and galacturonic acids), six amino acids (alanine, asparagine, aspartate, GABA, isoleucine, and valine), rhamnitol, p-coumaroyl derivative, phloretin/phloridzin and choline, as well as β-sitosterol, fatty acid chains, phosphatidylcholine, and digalactosyldiacylglycerol. Finally, the application of PCA analysis allowed us to highlight possible differences/similarities. The Magnana cultivar showed the highest content of sugars, GABA, valine, isoleucine, and alanine. The Runsé cultivar was characterized by high amounts of organic acids, whereas the Gamba Fina cultivar showed a high content of chlorogenic acid. A significant amount of quinic acid was detected in the Carla cultivar. The knowledge of apple chemical profiles can be useful for industries interested in specific compounds for obtaining ingredients of food supplements and functional foods and for promoting apple valorization and preservation.
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13
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Beckmann M, Wilson T, Lloyd AJ, Torres D, Goios A, Willis ND, Lyons L, Phillips H, Mathers JC, Draper J. Challenges Associated With the Design and Deployment of Food Intake Urine Biomarker Technology for Assessment of Habitual Diet in Free-Living Individuals and Populations-A Perspective. Front Nutr 2020; 7:602515. [PMID: 33344495 PMCID: PMC7745244 DOI: 10.3389/fnut.2020.602515] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 10/29/2020] [Indexed: 12/27/2022] Open
Abstract
Improvement of diet at the population level is a cornerstone of national and international strategies for reducing chronic disease burden. A critical challenge in generating robust data on habitual dietary intake is accurate exposure assessment. Self-reporting instruments (e.g., food frequency questionnaires, dietary recall) are subject to reporting bias and serving size perceptions, while weighed dietary assessments are unfeasible in large-scale studies. However, secondary metabolites derived from individual foods/food groups and present in urine provide an opportunity to develop potential biomarkers of food intake (BFIs). Habitual dietary intake assessment in population surveys using biomarkers presents several challenges, including the need to develop affordable biofluid collection methods, acceptable to participants that allow collection of informative samples. Monitoring diet comprehensively using biomarkers requires analytical methods to quantify the structurally diverse mixture of target biomarkers, at a range of concentrations within urine. The present article provides a perspective on the challenges associated with the development of urine biomarker technology for monitoring diet exposure in free-living individuals with a view to its future deployment in "real world" situations. An observational study (n = 95), as part of a national survey on eating habits, provided an opportunity to explore biomarker measurement in a free-living population. In a second food intervention study (n = 15), individuals consumed a wide range of foods as a series of menus designed specifically to achieve exposure reflecting a diversity of foods commonly consumed in the UK, emulating normal eating patterns. First Morning Void urines were shown to be suitable samples for biomarker measurement. Triple quadrupole mass spectrometry, coupled with liquid chromatography, was used to assess simultaneously the behavior of a panel of 54 potential BFIs. This panel of chemically diverse biomarkers, reporting intake of a wide range of commonly-consumed foods, can be extended successfully as new biomarker leads are discovered. Towards validation, we demonstrate excellent discrimination of eating patterns and quantitative relationships between biomarker concentrations in urine and the intake of several foods. In conclusion, we believe that the integration of information from BFI technology and dietary self-reporting tools will expedite research on the complex interactions between dietary choices and health.
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Affiliation(s)
- Manfred Beckmann
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Thomas Wilson
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Amanda J. Lloyd
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Duarte Torres
- Faculty of Nutrition and Food Sciences, University of Porto, Porto, Portugal
- Epidemiology Research Unit (EPIUnit), Institute of Public Health, University of Porto, Porto, Portugal
| | - Ana Goios
- Faculty of Nutrition and Food Sciences, University of Porto, Porto, Portugal
- Epidemiology Research Unit (EPIUnit), Institute of Public Health, University of Porto, Porto, Portugal
| | - Naomi D. Willis
- Human Nutrition Research Centre, Population Health Sciences Institute, William Leech Building, Newcastle University, Newcastle-upon-Tyne, United Kingdom
| | - Laura Lyons
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Helen Phillips
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - John C. Mathers
- Human Nutrition Research Centre, Population Health Sciences Institute, William Leech Building, Newcastle University, Newcastle-upon-Tyne, United Kingdom
| | - John Draper
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
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14
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Willis ND, Lloyd AJ, Xie L, Stiegler M, Tailliart K, Garcia-Perez I, Chambers ES, Beckmann M, Draper J, Mathers JC. Design and Characterisation of a Randomized Food Intervention That Mimics Exposure to a Typical UK Diet to Provide Urine Samples for Identification and Validation of Metabolite Biomarkers of Food Intake. Front Nutr 2020; 7:561010. [PMID: 33195362 PMCID: PMC7609501 DOI: 10.3389/fnut.2020.561010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 09/07/2020] [Indexed: 12/12/2022] Open
Abstract
Poor dietary choices are major risk factors for obesity and non-communicable diseases, which places an increasing burden on healthcare systems worldwide. To monitor the effectiveness of healthy eating guidelines and strategies, there is a need for objective measures of dietary intake in community settings. Metabolites derived from specific foods present in urine samples can provide objective biomarkers of food intake (BFIs). Whilst the majority of biomarker discovery/validation studies have investigated potential biomarkers for single foods only, this study considered the whole diet by using menus that delivered a wide range of foods in meals that emulated conventional UK eating patterns. Fifty-one healthy participants (range 19-77 years; 57% female) followed a uniquely designed, randomized controlled dietary intervention, and provided spot urine samples suitable for discovery of BFIs within a real-world context. Free-living participants prepared and consumed all foods and drinks in their own homes and were asked to follow the protocols for meal consumption and home urine sample collection. This study also assessed the robustness, and impact on data quality, of a minimally invasive urine collection protocol. Overall the study design was well-accepted by participants and concluded successfully without any drop outs. Compliance for urine collection, adherence to menu plans, and observance of recommended meal timings, was shown to be very high. Metabolome analysis using mass spectrometry coupled with data mining demonstrated that the study protocol was well-suited for BFI discovery and validation. Novel, putative biomarkers for an extended range of foods were identified including legumes, curry, strongly-heated products, and artificially sweetened, low calorie beverages. In conclusion, aspects of this study design would help to overcome several current challenges in the development of BFI technology. One specific attribute was the examination of BFI generalizability across related food groups and across different preparations and cooking methods of foods. Furthermore, the collection of urine samples at multiple time points helped to determine which spot sample was optimal for identification and validation of BFIs in free-living individuals. A further valuable design feature centered on the comprehensiveness of the menu design which allowed the testing of biomarker specificity within a biobank of urine samples.
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Affiliation(s)
- Naomi D. Willis
- Human Nutrition Research Centre, Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, United Kingdom
| | - Amanda J. Lloyd
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Long Xie
- Human Nutrition Research Centre, Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, United Kingdom
| | - Martina Stiegler
- Human Nutrition Research Centre, Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, United Kingdom
| | - Kathleen Tailliart
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Isabel Garcia-Perez
- Nutrition and Dietetic Research Group, Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, United Kingdom
| | - Edward S. Chambers
- Nutrition and Dietetic Research Group, Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, United Kingdom
| | - Manfred Beckmann
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - John Draper
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - John C. Mathers
- Human Nutrition Research Centre, Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, United Kingdom
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15
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Identifying unknown metabolites using NMR-based metabolic profiling techniques. Nat Protoc 2020; 15:2538-2567. [PMID: 32681152 DOI: 10.1038/s41596-020-0343-3] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 04/20/2020] [Indexed: 01/20/2023]
Abstract
Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.
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16
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Posma JM, Garcia-Perez I, Frost G, Aljuraiban GS, Chan Q, Van Horn L, Daviglus M, Stamler J, Holmes E, Elliott P, Nicholson JK. Nutriome-metabolome relationships provide insights into dietary intake and metabolism. ACTA ACUST UNITED AC 2020; 1:426-436. [PMID: 32954362 PMCID: PMC7497842 DOI: 10.1038/s43016-020-0093-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Dietary assessment traditionally relies on self-reported data which are often inaccurate and may result in erroneous diet-disease risk associations. We illustrate how urinary metabolic phenotyping can be used as alternative approach for obtaining information on dietary patterns. We used two multi-pass 24-hr dietary recalls, obtained on two occasions on average three weeks apart, paired with two 24-hr urine collections from 1,848 U.S. individuals; 67 nutrients influenced the urinary metabotype measured with 1H-NMR spectroscopy characterized by 46 structurally identified metabolites. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living U.S. cohort and replicated in an independent U.K. cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers are associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to unhealthy metabolic phenotype and hence give entry points for prevention and intervention strategies.
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Affiliation(s)
- Joram M Posma
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, SW7 2AZ, U.K.,Health Data Research UK-London, U.K
| | - Isabel Garcia-Perez
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K
| | - Gary Frost
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K
| | - Ghadeer S Aljuraiban
- The Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia.,Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K
| | - Queenie Chan
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K.,MRC Centre for Environment and Health, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K
| | - Linda Van Horn
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, U.S.A
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL 60612
| | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, U.S.A
| | - Elaine Holmes
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K.,UK Dementia Research Institute, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K.,Division of Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia.,The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, WA 6150, Australia
| | - Paul Elliott
- Health Data Research UK-London, U.K.,Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K.,MRC Centre for Environment and Health, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K.,UK Dementia Research Institute, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K.,National Institute for Health Research Imperial Biomedical Research Centre, St. Mary's Campus, Imperial College London, W2 1PG, U.K.,British Heart Foundation Centre of Research Excellence at Imperial, Imperial College London, W2 1PG, U.K
| | - Jeremy K Nicholson
- Division of Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia.,The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, WA 6150, Australia
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17
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Rodriguez-Martinez A, Ayala R, Posma JM, Harvey N, Jiménez B, Sonomura K, Sato TA, Matsuda F, Zalloua P, Gauguier D, Nicholson JK, Dumas ME. pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra. Bioinformatics 2020; 35:1916-1922. [PMID: 30351417 PMCID: PMC6546129 DOI: 10.1093/bioinformatics/bty837] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 08/24/2018] [Accepted: 10/22/2018] [Indexed: 01/21/2023] Open
Abstract
Motivation Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra. Results The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building Availability and implementation The algorithm is implemented using the MWASTools R/Bioconductor package. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrea Rodriguez-Martinez
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.,Department of Epidemiology and Biostatistics School of Public Health, Imperial College London, London, UK
| | - Rafael Ayala
- Section of Structural Biology, Department of Medicine, Shimadzu Corporation, Kyoto, Japan
| | - Joram M Posma
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.,Department of Epidemiology and Biostatistics School of Public Health, Imperial College London, London, UK
| | - Nikita Harvey
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Beatriz Jiménez
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Kazuhiro Sonomura
- Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, Japan.,Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Taka-Aki Sato
- Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, Japan.,Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Pierre Zalloua
- School of Medicine, Lebanese American University, Beirut, Lebanon
| | - Dominique Gauguier
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Cordeliers Research Centre, INSERM UMR_S, Paris, France
| | - Jeremy K Nicholson
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Marc-Emmanuel Dumas
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK
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18
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Charris-Molina A, Riquelme G, Burdisso P, Hoijemberg PA. Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D 1H NMR Database Search. J Proteome Res 2020; 19:2977-2988. [PMID: 32450699 DOI: 10.1021/acs.jproteome.9b00872] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
NMR-based metabolomics requires proper identification of metabolites to draw conclusions from the system under study. Normally, multivariate data analysis is performed using 1D 1H NMR spectra, and identification of peaks (and then compounds) relevant to the classification is accomplished using database queries as a first step. 1D 1H NMR spectra of complex mixtures often suffer from peak overlap. To overcome this issue, several studies employed the projections of the (tilted and symmetrized) 2D 1H J-resolved (JRES) spectra, p-JRES, which are similar to 1D 1H decoupled spectra. Nonetheless, there are no public databases available that allow searching for chemical shift spectral data for multiplets. We present the Chemical Shift Multiplet Database (CSMDB), built utilizing JRES spectra obtained from the Birmingham Metabolite Library. The CSMDB provides scoring accounting for both matched and unmatched peaks from a query list and the database hits. This input list is generated from a projection of a 2D statistical correlation analysis on the JRES spectra, p-(JRES-STOCSY), being able to compare the multiplets for the matched peaks, in essence, the f1 traces from the JRES-STOCSY spectrum and from the database hit. The inspection of the unmatched peaks for the database hit allows the retrieval of peaks in the query list that have a decreased correlation coefficient due to low intensities. The CSMDB is coupled to "ConQuer ABC", which permits the assessment of biological correlation by means of consecutive queries with the unmatched peaks in the first and subsequent queries.
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Affiliation(s)
- Andrés Charris-Molina
- Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Ciudad Universitaria, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina.,CIBION-CONICET, Centro de Investigaciones en Bionanociencias, NMR Group, Polo Científico Tecnológico, Ciudad Autónoma de Buenos Aires, Buenos Aires C1425FQD, Argentina
| | - Gabriel Riquelme
- Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Ciudad Universitaria, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina.,CIBION-CONICET, Centro de Investigaciones en Bionanociencias, NMR Group, Polo Científico Tecnológico, Ciudad Autónoma de Buenos Aires, Buenos Aires C1425FQD, Argentina
| | - Paula Burdisso
- Instituto de Biología Molecular y Celular de Rosario (IBR-CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario and Plataforma Argentina de Biología Estructural y Metabolómica (PLABEM), Rosario, Santa Fe S2002LRK, Argentina
| | - Pablo A Hoijemberg
- CIBION-CONICET, Centro de Investigaciones en Bionanociencias, NMR Group, Polo Científico Tecnológico, Ciudad Autónoma de Buenos Aires, Buenos Aires C1425FQD, Argentina.,ECyT-UNSAM, 25 de Mayo y Francia, San Martín, Buenos Aires B1650HMP, Argentina
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19
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Garcia-Perez I, Posma JM, Chambers ES, Mathers JC, Draper J, Beckmann M, Nicholson JK, Holmes E, Frost G. RETRACTED ARTICLE: Dietary metabotype modelling predicts individual responses to dietary interventions. NATURE FOOD 2020; 1:355-364. [PMID: 37128097 DOI: 10.1038/s43016-020-0092-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 05/06/2020] [Indexed: 12/12/2022]
Abstract
Habitual consumption of poor quality diets is linked directly to risk factors for many non-communicable diseases. This has resulted in the vast majority of countries and the World Health Organization developing policies for healthy eating to reduce the prevalence of non-communicable diseases in the population. However, there is mounting evidence of variability in individual metabolic responses to any dietary intervention. We have developed a method for applying a pipeline for understanding interindividual differences in response to diet, based on coupling data from highly controlled dietary studies with deep metabolic phenotyping. In this feasibility study, we create an individual Dietary Metabotype Score (DMS) that embodies interindividual variability in dietary response and captures consequent dynamic changes in concentrations of urinary metabolites. We find an inverse relationship between the DMS and blood glucose concentration. There is also a relationship between the DMS and urinary metabolic energy loss. Furthermore, we use a metabolic entropy approach to visualize individual and collective responses to dietary interventions. Potentially, the DMS offers a method to target and to enhance dietary response at the individual level, thereby reducing the burden of non-communicable diseases at the population level.
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Affiliation(s)
- Isabel Garcia-Perez
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Joram M Posma
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Health Data Research UK-London, London, UK
| | - Edward S Chambers
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - John C Mathers
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - John Draper
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - Manfred Beckmann
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - Jeremy K Nicholson
- The Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
| | - Elaine Holmes
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
- The Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
| | - Gary Frost
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
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20
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McNamara AE, Collins C, Harsha PSCS, González-Peña D, Gibbons H, McNulty BA, Nugent AP, Walton J, Flynn A, Brennan L. Metabolomic-Based Approach to Identify Biomarkers of Apple Intake. Mol Nutr Food Res 2020; 64:e1901158. [PMID: 32243719 DOI: 10.1002/mnfr.201901158] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 03/16/2020] [Indexed: 01/18/2023]
Abstract
SCOPE There is an increased interest in developing biomarkers of food intake to address some of the limitations associated with self-reported data. The objective is to identify biomarkers of apple intake, examine dose-response relationships, and agreement with self-reported data. METHODS AND RESULTS Metabolomic data from three studies are examined: an acute intervention, a short-term intervention, and a free-living cohort study. Fasting and postprandial urine samples are collected for analysis by 1 H-NMR and liquid chromatography-mass spectrometry (LC-MS). Calibration curves are developed to determine apple intake and classify individuals into categories of intake. Multivariate analysis of data reveals that levels of multiple metabolites increase significantly post-apple consumption, compared to the control food-broccoli. In the dose-response study, urinary xylose, epicatechin sulfate, and 2,6-dimethyl-2-(2-hydroxyethyl)-3,4-dihydro-2H-1-benzopyran increase as apple intake increases. Urinary xylose concentrations in a free-living cohort perform poorly at an individual level but are capable of ranking individuals in categories of intake. CONCLUSION Urinary xylose exhibits a dose-response relationship with apple intake and performs well as a ranking biomarker in the population study. Other potential biomarkers are identified and future work will combine these with xylose in a biomarker panel which may allow for a more objective determination of individual intake.
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Affiliation(s)
- Aoife E McNamara
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Cassandra Collins
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Pedapati S C Sri Harsha
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Diana González-Peña
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Helena Gibbons
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Breige A McNulty
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland
| | - Anne P Nugent
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.,Institute for Global Food Security, School of Biological Sciences, Queens University Belfast, Belfast, Northern Ireland
| | - Janette Walton
- School of Food and Nutritional Sciences, University College Cork, Cork, Ireland.,Department of Biological Sciences, Cork Institute of Technology, Cork, Ireland
| | - Albert Flynn
- School of Food and Nutritional Sciences, University College Cork, Cork, Ireland
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
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21
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Coras R, Murillo-Saich JD, Guma M. Circulating Pro- and Anti-Inflammatory Metabolites and Its Potential Role in Rheumatoid Arthritis Pathogenesis. Cells 2020; 9:E827. [PMID: 32235564 PMCID: PMC7226773 DOI: 10.3390/cells9040827] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/16/2020] [Accepted: 03/18/2020] [Indexed: 12/11/2022] Open
Abstract
Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that affects synovial joints, leading to inflammation, joint destruction, loss of function, and disability. Although recent pharmaceutical advances have improved the treatment of RA, patients often inquire about dietary interventions to improve RA symptoms, as they perceive pain and/or swelling after the consumption or avoidance of certain foods. There is evidence that some foods have pro- or anti-inflammatory effects mediated by diet-related metabolites. In addition, recent literature has shown a link between diet-related metabolites and microbiome changes, since the gut microbiome is involved in the metabolism of some dietary ingredients. But diet and the gut microbiome are not the only factors linked to circulating pro- and anti-inflammatory metabolites. Other factors including smoking, associated comorbidities, and therapeutic drugs might also modify the circulating metabolomic profile and play a role in RA pathogenesis. This article summarizes what is known about circulating pro- and anti-inflammatory metabolites in RA. It also emphasizes factors that might be involved in their circulating concentrations and diet-related metabolites with a beneficial effect in RA.
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Affiliation(s)
- Roxana Coras
- Department of Medicine, School of Medicine, University of California, San Diego, 9500 Gilman Drive, San Diego, CA 92093, USA; (R.C.); (J.D.M.-S.)
- Department of Medicine, Autonomous University of Barcelona, Plaça Cívica, 08193 Bellaterra, Barcelona, Spain
| | - Jessica D. Murillo-Saich
- Department of Medicine, School of Medicine, University of California, San Diego, 9500 Gilman Drive, San Diego, CA 92093, USA; (R.C.); (J.D.M.-S.)
| | - Monica Guma
- Department of Medicine, School of Medicine, University of California, San Diego, 9500 Gilman Drive, San Diego, CA 92093, USA; (R.C.); (J.D.M.-S.)
- Department of Medicine, Autonomous University of Barcelona, Plaça Cívica, 08193 Bellaterra, Barcelona, Spain
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22
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Ocvirk S, Wilson AS, Posma JM, Li JV, Koller KR, Day GM, Flanagan CA, Otto JE, Sacco PE, Sacco FD, Sapp FR, Wilson AS, Newton K, Brouard F, DeLany JP, Behnning M, Appolonia CN, Soni D, Bhatti F, Methé B, Fitch A, Morris A, Gaskins HR, Kinross J, Nicholson JK, Thomas TK, O'Keefe SJD. A prospective cohort analysis of gut microbial co-metabolism in Alaska Native and rural African people at high and low risk of colorectal cancer. Am J Clin Nutr 2020; 111:406-419. [PMID: 31851298 PMCID: PMC6997097 DOI: 10.1093/ajcn/nqz301] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/14/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Alaska Native (AN) people have the world's highest recorded incidence of sporadic colorectal cancer (CRC) (∼91:100,000), whereas rural African (RA) people have the lowest risk (<5:100,000). Previous data supported the hypothesis that diet affected CRC risk through its effects on the colonic microbiota that produce tumor-suppressive or -promoting metabolites. OBJECTIVES We investigated whether differences in these metabolites may contribute to the high risk of CRC in AN people. METHODS A cross-sectional observational study assessed dietary intake from 32 AN and 21 RA healthy middle-aged volunteers before screening colonoscopy. Analysis of fecal microbiota composition by 16S ribosomal RNA gene sequencing and fecal/urinary metabolites by 1H-NMR spectroscopy was complemented with targeted quantification of fecal SCFAs, bile acids, and functional microbial genes. RESULTS Adenomatous polyps were detected in 16 of 32 AN participants, but not found in RA participants. The AN diet contained higher proportions of fat and animal protein and less fiber. AN fecal microbiota showed a compositional predominance of Blautia and Lachnoclostridium, higher microbial capacity for bile acid conversion, and low abundance of some species involved in saccharolytic fermentation (e.g., Prevotellaceae, Ruminococcaceae), but no significant lack of butyrogenic bacteria. Significantly lower concentrations of tumor-suppressive butyrate (22.5 ± 3.1 compared with 47.2 ± 7.3 SEM µmol/g) coincided with significantly higher concentrations of tumor-promoting deoxycholic acid (26.7 ± 4.2 compared with 11 ± 1.9 µmol/g) in AN fecal samples. AN participants had lower quantities of fecal/urinary metabolites than RA participants and metabolite profiles correlated with the abundance of distinct microbial genera in feces. The main microbial and metabolic CRC-associated markers were not significantly altered in AN participants with adenomatous polyps. CONCLUSIONS The low-fiber, high-fat diet of AN people and exposure to carcinogens derived from diet or environment are associated with a tumor-promoting colonic milieu as reflected by the high rates of adenomatous polyps in AN participants.
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Affiliation(s)
- Soeren Ocvirk
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Gastrointestinal Microbiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Annette S Wilson
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joram M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom
| | - Jia V Li
- Section of Nutritional Research, Division of Digestive Diseases, Department of Metabolism, Digestion, and Reproduction, Imperial College, London, United Kingdom
- Centre for Digestive and Gut Health, Institution of Global Health Innovation, Imperial College, London, United Kingdom
| | - Kathryn R Koller
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Gretchen M Day
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Christie A Flanagan
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Jill Evon Otto
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Pam E Sacco
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Frank D Sacco
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Flora R Sapp
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Amy S Wilson
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Keith Newton
- Division of Gastroenterology, University of KwaZulu-Natal, Durban, South Africa
| | - Faye Brouard
- Manguzi Hospital, Manguzi, KwaZulu-Natal, South Africa
| | - James P DeLany
- Division of Endocrinology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Translational Research Institute for Metabolism and Diabetes, Advent Health, Orlando, FL, USA
| | - Marissa Behnning
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Corynn N Appolonia
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Devavrata Soni
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Faheem Bhatti
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Barbara Methé
- Center for Medicine and the Microbiome, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Adam Fitch
- Center for Medicine and the Microbiome, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alison Morris
- Center for Medicine and the Microbiome, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - H Rex Gaskins
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - James Kinross
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Jeremy K Nicholson
- Centre for Digestive and Gut Health, Institution of Global Health Innovation, Imperial College, London, United Kingdom
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Timothy K Thomas
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Stephen J D O'Keefe
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
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23
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Wilson T, Garcia-Perez I, Posma JM, Lloyd AJ, Chambers ES, Tailliart K, Zubair H, Beckmann M, Mathers JC, Holmes E, Frost G, Draper J. Spot and Cumulative Urine Samples Are Suitable Replacements for 24-Hour Urine Collections for Objective Measures of Dietary Exposure in Adults Using Metabolite Biomarkers. J Nutr 2019; 149:1692-1700. [PMID: 31240300 DOI: 10.1093/jn/nxz138] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 04/17/2019] [Accepted: 05/24/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Measurement of multiple food intake exposure biomarkers in urine may offer an objective method for monitoring diet. The potential of spot and cumulative urine samples that have reduced burden on participants as replacements for 24-h urine collections has not been evaluated. OBJECTIVE The aim of this study was to determine the utility of spot and cumulative urine samples for classifying the metabolic profiles of people according to dietary intake when compared with 24-h urine collections in a controlled dietary intervention study. METHODS Nineteen healthy individuals (10 male, 9 female, aged 21-65 y, BMI 20-35 kg/m2) each consumed 4 distinctly different diets, each for 1 wk. Spot urine samples were collected ∼2 h post meals on 3 intervention days/wk. Cumulative urine samples were collected daily over 3 separate temporal periods. A 24-h urine collection was created by combining the 3 cumulative urine samples. Urine samples were analyzed with metabolite fingerprinting by both high-resolution flow infusion electrospray mass spectrometry (FIE-HRMS) and proton nuclear magnetic resonance spectroscopy (1H-NMR). Concentrations of dietary intake biomarkers were measured with liquid chromatography triple quadrupole mass spectrometry and by integration of 1H-NMR data. RESULTS Cross-validation modeling with 1H-NMR and FIE-HRMS data demonstrated the power of spot and cumulative urine samples in predicting dietary patterns in 24-h urine collections. Particularly, there was no significant loss of information when post-dinner (PD) spot or overnight cumulative samples were substituted for 24-h urine collections (classification accuracies of 0.891 and 0.938, respectively). Quantitative analysis of urine samples also demonstrated the relation between PD spot samples and 24-h urines for dietary exposure biomarkers. CONCLUSIONS We conclude that PD spot urine samples are suitable replacements for 24-h urine collections. Alternatively, cumulative samples collected overnight predict similarly to 24-h urine samples and have a lower collection burden for participants.
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Affiliation(s)
- Thomas Wilson
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Isabel Garcia-Perez
- Section of Biomolecular Medicine, Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Joram M Posma
- Section of Biomolecular Medicine, Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Amanda J Lloyd
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Edward S Chambers
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, United Kingdom
| | - Kathleen Tailliart
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Hassan Zubair
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Manfred Beckmann
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - John C Mathers
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Elaine Holmes
- Section of Biomolecular Medicine, Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Gary Frost
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, United Kingdom
| | - John Draper
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
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24
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McGill D, Chekmeneva E, Lindon JC, Takats Z, Nicholson JK. Application of novel solid phase extraction-NMR protocols for metabolic profiling of human urine. Faraday Discuss 2019; 218:395-416. [PMID: 31116193 DOI: 10.1039/c8fd00220g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Metabolite identification and annotation procedures are necessary for the discovery of biomarkers indicative of phenotypes or disease states, but these processes can be bottlenecked by the sheer complexity of biofluids containing thousands of different compounds. Here we describe low-cost novel SPE-NMR protocols utilising different cartridges and conditions, on both natural and artificial urine mixtures, which produce unique retention profiles useful for metabolic profiling. We find that different SPE methods applied to biofluids such as urine can be used to selectively retain metabolites based on compound taxonomy or other key functional groups, reducing peak overlap through concentration and fractionation of unknowns and hence promising greater control over the metabolite annotation/identification process.
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Affiliation(s)
- Daniel McGill
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
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25
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Whiley L, Chekmeneva E, Berry DJ, Jiménez B, Yuen AHY, Salam A, Hussain H, Witt M, Takats Z, Nicholson J, Lewis MR. Systematic Isolation and Structure Elucidation of Urinary Metabolites Optimized for the Analytical-Scale Molecular Profiling Laboratory. Anal Chem 2019; 91:8873-8882. [PMID: 31188566 PMCID: PMC6666900 DOI: 10.1021/acs.analchem.9b00241] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Annotation
and identification of metabolite biomarkers is critical
for their biological interpretation in metabolic phenotyping studies,
presenting a significant bottleneck in the successful implementation
of untargeted metabolomics. Here, a systematic multistep protocol
was developed for the purification and de novo structural elucidation
of urinary metabolites. The protocol is most suited for instances
where structure elucidation and metabolite annotation are critical
for the downstream biological interpretation of metabolic phenotyping
studies. First, a bulk urine pool was desalted using ion-exchange
resins enabling large-scale fractionation using precise iterations
of analytical scale chromatography. Primary urine fractions were collected
and assembled into a “fraction bank” suitable for long-term
laboratory storage. Secondary and tertiary fractionations exploited
differences in selectivity across a range of reversed-phase chemistries,
achieving the purification of metabolites of interest yielding an
amount of material suitable for chemical characterization. To exemplify
the application of the systematic workflow in a diverse set of cases,
four metabolites with a range of physicochemical properties were selected
and purified from urine and subjected to chemical formula and structure
elucidation by respective magnetic resonance mass spectrometry (MRMS)
and NMR analyses. Their structures were fully assigned as tetrahydropentoxyline,
indole-3-acetic-acid-O-glucuronide, p-cresol glucuronide, and pregnanediol-3-glucuronide. Unused effluent
was collected, dried, and returned to the fraction bank, demonstrating
the viability of the system for repeat use in metabolite annotation
with a high degree of efficiency.
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Affiliation(s)
- Luke Whiley
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom.,UK Dementia Research Institute , Imperial College London, Hammersmith Hospital , Burlington Danes Building , London , W12 0NN , United Kingdom
| | - Elena Chekmeneva
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom
| | - David J Berry
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom
| | - Beatriz Jiménez
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom
| | - Ada H Y Yuen
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom
| | - Ash Salam
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom
| | - Humma Hussain
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom
| | - Matthias Witt
- Bruker Daltonik GmbH , MRMS Solutions , 28359 Bremen , Germany
| | - Zoltan Takats
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom
| | - Jeremy Nicholson
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom
| | - Matthew R Lewis
- The MRC-NIHR National Phenome Centre and Imperial BRC Clinical Phenotyping Centre , Imperial College London , London , W12 0NN , United Kingdom
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26
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Lloyd AJ, Willis ND, Wilson T, Zubair H, Chambers E, Garcia-Perez I, Xie L, Tailliart K, Beckmann M, Mathers JC, Draper J. Addressing the pitfalls when designing intervention studies to discover and validate biomarkers of habitual dietary intake. Metabolomics 2019; 15:72. [PMID: 31049735 PMCID: PMC6497620 DOI: 10.1007/s11306-019-1532-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 04/19/2019] [Indexed: 12/21/2022]
Abstract
INTRODUCTION Dietary exposure monitoring within populations is reliant on self-reported measures such as Food Frequency Questionnaires and diet diaries. These methods often contain inaccurate information due to participant misreporting, non-compliance and bias. Urinary metabolites derived from individual foods could provide additional objective indicators of dietary exposure. For biomarker approaches to have utility it is essential that they cover a wide-range of commonly consumed foods and the methodology works in a real-world environment. OBJECTIVES To test that the methodology works in a real-world environment and to consider the impact of the major sources of likely variance; particularly complex meals, different food formulations, processing and cooking methods, as well as the dynamics of biomarker duration in the body. METHODS We designed and tested a dietary exposure biomarker discovery and validation strategy based on a food intervention study involving free-living individuals preparing meals and collecting urine samples at home. Two experimental periods were built around three consecutive day menu plans where all foods and drinks were provided (n = 15 and n = 36). RESULTS The experimental design was validated by confirming known consumption biomarkers in urinary samples after the first menu plan. We tested biomarker performance with different food formulations and processing methods involving meat, wholegrain, fruits and vegetables. CONCLUSION It was demonstrated that spot urine samples, together with robust dietary biomarkers, despite major sources of variance, could be used successfully for dietary exposure monitoring in large epidemiological studies.
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Affiliation(s)
- A J Lloyd
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3DA, UK
| | - N D Willis
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
| | - T Wilson
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3DA, UK
| | - H Zubair
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3DA, UK
| | - E Chambers
- Nutrition and Dietetic Research Group, Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Hammersmith Hospital Campus, Imperial College London, London, W12 0NN, UK
| | - I Garcia-Perez
- Nutrition and Dietetic Research Group, Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Hammersmith Hospital Campus, Imperial College London, London, W12 0NN, UK
| | - L Xie
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
| | - K Tailliart
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3DA, UK
| | - M Beckmann
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3DA, UK
| | - J C Mathers
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
| | - J Draper
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3DA, UK.
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Charris-Molina A, Riquelme G, Burdisso P, Hoijemberg PA. Tackling the Peak Overlap Issue in NMR Metabolomics Studies: 1D Projected Correlation Traces from Statistical Correlation Analysis on Nontilted 2D 1H NMR J-Resolved Spectra. J Proteome Res 2019; 18:2241-2253. [PMID: 30916564 DOI: 10.1021/acs.jproteome.9b00093] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The identification of metabolites in complex biological matrices is a challenging task in 1D 1H-NMR-based metabolomics studies. Statistical total correlation spectroscopy (STOCSY) has emerged for aiding the structural elucidation by revealing the peaks that present a high correlation to a driver peak of interest (which would likely belong to the same molecule). However, in these studies, the signals from metabolites are normally present as a mixture of overlapping resonances, limiting the performance of STOCSY. As an alternative to avoid the overlap issue, 2D 1H homonuclear J-resolved (JRES) spectra were projected, in their usual tilted and symmetrized processed form, and STOCSY was applied on these 1D projections (p-JRES-STOCSY). Nonetheless, this approach suffers in cases where the signals are very close. In addition, STOCSY was applied to the whole JRES spectra (also tilted) to identify correlated multiplets, although the overlap issue in itself was not addressed directly and the subsequent search in databases is complicated in cases of higher order coupling. With these limitations in mind, in the present work, we propose a new methodology based on the application of STOCSY on a set of nontilted JRES spectra, detecting peaks that would overlap in 1D spectra of the same sample set. Correlation comparison analysis for peak overlap detection (COCOA-POD) is able to reconstruct projected 1D STOCSY traces that result in more suitable database queries, as all peaks are summed at their f2 resonances instead of the resonance corresponding to the multiplet center in the tilted JRES spectra. (The peak dispersion and resolution enhancement gained are not sacrificed by the projection.) Besides improving database queries with better peak lists obtained from the projections of the 2D STOCSY analysis, the overlap region is examined, and the multiplet itself is analyzed from the correlation trace at 45° to obtain a cleaner multiplet profile, free from contributions from uncorrelated neighboring peaks.
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Affiliation(s)
- Andrés Charris-Molina
- CIBION-CONICET, Centro de Investigaciones en Bionanociencias , NMR Group , Polo Científico Tecnológico , Ciudad Autónoma de Buenos Aires C1425FQD , Argentina.,Universidad de Buenos Aires , Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales , Ciudad Universitaria, Buenos Aires C1428EGA , Argentina
| | - Gabriel Riquelme
- CIBION-CONICET, Centro de Investigaciones en Bionanociencias , NMR Group , Polo Científico Tecnológico , Ciudad Autónoma de Buenos Aires C1425FQD , Argentina.,Universidad de Buenos Aires , Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales , Ciudad Universitaria, Buenos Aires C1428EGA , Argentina
| | - Paula Burdisso
- Instituto de Biología Molecular y Celular de Rosario (IBR-CONICET) , Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario and Plataforma Argentina de Biología Estructural y Metabolómica (PLABEM) , Rosario , Santa Fe S2002LRK , Argentina
| | - Pablo A Hoijemberg
- CIBION-CONICET, Centro de Investigaciones en Bionanociencias , NMR Group , Polo Científico Tecnológico , Ciudad Autónoma de Buenos Aires C1425FQD , Argentina.,ECyT-UNSAM , 25 de Mayo y Francia , San Martín, Buenos Aires B1650HMP , Argentina
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González-Peña D, Brennan L. Recent Advances in the Application of Metabolomics for Nutrition and Health. Annu Rev Food Sci Technol 2019; 10:479-519. [DOI: 10.1146/annurev-food-032818-121715] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.
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Affiliation(s)
- Diana González-Peña
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland;,
| | - Lorraine Brennan
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland;,
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Lynn KS, Cheng ML, Yang HC, Liang YJ, Kang MJ, Chen FL, Shiao MS, Pan WH. Vegetable Signatures Derived from Human Urinary Metabolomic Data in Controlled Feeding Studies. J Proteome Res 2019; 18:159-168. [PMID: 30517004 DOI: 10.1021/acs.jproteome.8b00470] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Examination of changes in urinary metabolomic profiles after vegetable ingestion may lead to new methods of assessing plant food intake. To this regard, we developed a proof-of-principle methodology to identify urinary metabolomic signatures for spinach, celery, and onion. Three feeding studies were conducted. In the first study, healthy individuals were fed with spinach, celery, onion, and no vegetables in four separate experiments with pooled urinary samples for metabolite discovery. The same protocol was used to validate the finding at the individual level in the second study and when feeding all three vegetables simultaneously in the third study. An LC-MS-based metabolomics approach was adopted to search for indicative metabolites from urine samples collected during multiple time periods before and after the meal. Consequently, a total of 1, 9, and 3 nonoverlapping urinary metabolites were associated with the intake of spinach, celery, and onion, respectively. The PCA signature of these metabolites followed a similar "time cycle" pattern, which maximized at approximately 2-4 h after intake. In addition, the metabolite profiles for the same vegetable were consistent across samples, regardless of whether it was consumed individually or in combination. The developed methodology along with the identified urinary metabolomic signatures were potential tools for assessing plant food intake.
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Affiliation(s)
- Ke-Shiuan Lynn
- Department of Mathematics , Fu Jen Catholic University , New Taipei City 24205 , Taiwan
| | - Mei-Ling Cheng
- Department of Biomedical Sciences, College of Medicine , Chang Gung University , Taoyuan 33302 , Taiwan.,Metabolomics Core Laboratory, Healthy Aging Research Center , Chang Gung University , Taoyuan 33302 , Taiwan.,Clinical Metabolomics Core Laboratory , Chang Gung Memorial Hospital , Taoyuan 33305 , Taiwan
| | - Hsin-Chou Yang
- Institute of Statistical Science , Academia Sinica , Taipei 11529 , Taiwan
| | - Yu-Jen Liang
- Institute of Statistical Science , Academia Sinica , Taipei 11529 , Taiwan
| | - Mei-Jyh Kang
- Institute of Biomedical Sciences , Academia Sinica , Taipei 11529 , Taiwan
| | - Fong-Ling Chen
- Institute of Biomedical Sciences , Academia Sinica , Taipei 11529 , Taiwan
| | - Ming-Shi Shiao
- Department of Biomedical Sciences, College of Medicine , Chang Gung University , Taoyuan 33302 , Taiwan
| | - Wen-Harn Pan
- Institute of Biomedical Sciences , Academia Sinica , Taipei 11529 , Taiwan.,Institute of Population Health Sciences , National Health Research Institutes , Miaoli 35053 , Taiwan
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Joesten WC, Kennedy MA. RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies. Metabolomics 2019; 15:5. [PMID: 30830432 DOI: 10.1007/s11306-018-1465-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 12/20/2018] [Indexed: 12/18/2022]
Abstract
INTRODUCTION The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The "putatively annotated compound" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of "putatively annotated compound" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data. OBJECTIVES To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to "putatively annotated compound" assignments in NMR-based metabolic profiling studies. METHODS The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment. RESULTS Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to "putatively annotated compounds" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories. CONCLUSION RANCM is intended to reduce the ambiguity in "putatively annotated compound" assignments, to facilitate effective communication of the degree of confidence in "putatively annotated compound" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.
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Affiliation(s)
- William C Joesten
- Department of Chemistry and Biochemistry, Miami University, 106 Hughes Laboratories, Oxford, OH, 45056, USA
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Miami University, 106 Hughes Laboratories, Oxford, OH, 45056, USA.
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31
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Praticò G, Gao Q, Manach C, Dragsted LO. Biomarkers of food intake for Allium vegetables. GENES & NUTRITION 2018; 13:34. [PMID: 30607216 PMCID: PMC6309086 DOI: 10.1186/s12263-018-0624-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 11/30/2018] [Indexed: 11/24/2022]
Abstract
Allium vegetables are widely consumed around the world and are known for their potential bioactive components improving human health. These effects have been extensively investigated; however, the results were inconsistent in human studies. Biomarkers of food intake (BFIs) could provide objective measurements of food intake in observational studies and assess compliance in intervention studies. Therefore, the discovery and application of BFIs for Allium vegetables would facilitate the exploring and understanding of the health benefit of Allium vegetables. In this manuscript, we reviewed the currently used and potential candidate BFIs for Allium vegetables and evaluated their levels of validation. S-Allylmercapturic acid (ALMA), allyl methyl sulfide (AMS), allyl methyl sulfoxide (AMSO), allyl methyl sulfone (AMSO2), and S-allylcysteine (SAC), which are derived from organosulfur compounds, were shown to be promising candidate BFIs for garlic consumption. Further validation is needed to assess their robustness and concordance with other measures. Their applicability for the whole food group should be evaluated as well. N-Acetyl-S-(2-carboxypropyl)cysteine (CPMA) was detected in high levels in urine after both garlic and onion intake, suggesting that it may be used for the assessment of intake of Allium food group. The available information regarding its kinetics, robustness, and analytical performance is limited and needs to be assessed in further studies. No candidate BFIs specific to intake of onion, leek, chives, shallots, or ramsons were found. Untargeted metabolomics studies and further validation studies should be performed to discover more reliable BFIs for individual Allium vegetables and the whole food group. This paper serves as an example of Biomarker of Food Intake Reviews (BFIRev) and biomarker of food intake validation procedures.
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Affiliation(s)
- Giulia Praticò
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
- Department of Food Science, University of Copenhagen, Copenhagen, Denmark
| | - Qian Gao
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Claudine Manach
- INRA, Human Nutrition Unit, Université Clermont Auvergne, F63000 INRA, Clermont-Ferrand, France
| | - Lars O. Dragsted
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
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32
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S C Sri Harsha P, Abdul Wahab R, Cuparencu C, Dragsted LO, Brennan L. A Metabolomics Approach to the Identification of Urinary Biomarkers of Pea Intake. Nutrients 2018; 10:E1911. [PMID: 30518059 PMCID: PMC6315433 DOI: 10.3390/nu10121911] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 11/24/2018] [Accepted: 11/29/2018] [Indexed: 01/26/2023] Open
Abstract
A significant body of evidence demonstrates that isoflavone metabolites are good markers of soy intake, while research is lacking on specific markers of other leguminous sources such as peas. In this context, the objective of our current study was to identify biomarkers of pea intake using an untargeted metabolomics approach. A randomized cross-over acute intervention study was conducted on eleven participants who consumed peas and couscous (control food) in random order. The urine samples were collected in fasting state and postprandially at regular intervals and were further analysed by ultra-performance liquid chromatography coupled to quadrupole time of flight mass spectrometry (UPLC-QTOF-MS). Multivariate statistical analysis resulted in robust Partial least squares Discriminant Analysis (PLS-DA) models obtained for comparison of fasting against the postprandial time points (0 h vs. 4 h, (R²X = 0.41, Q² = 0.4); 0 h vs. 6 h, ((R²X = 0.517, Q² = 0.495)). Variables with variable importance of projection (VIP) scores ≥1.5 obtained from the PLS-DA plot were considered discriminant between the two time points. Repeated measures analysis of variance (ANOVA) was performed to identify features with a significant time effect. Assessment of the time course profile revealed that ten features displayed a differential time course following peas consumption compared to the control food. The interesting features were tentatively identified using accurate mass data and confirmed by tandem mass spectrometry (MS using commercial spectral databases and authentic standards. 2-Isopropylmalic acid, asparaginyl valine and N-carbamoyl-2-amino-2-(4-hydroxyphenyl) acetic acid were identified as markers reflecting pea intake. The three markers also increased in a dose-dependent manner in a randomized intervention study and were further confirmed in an independent intervention study. Overall, key validation criteria were met for the successfully identified pea biomarkers. Future work will examine their use in nutritional epidemiology studies.
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Affiliation(s)
- Pedapati S C Sri Harsha
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Roshaida Abdul Wahab
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Catalina Cuparencu
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 1958 Frederiksberg C, Denmark.
| | - Lars Ove Dragsted
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 1958 Frederiksberg C, Denmark.
| | - Lorraine Brennan
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland.
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Sri Harsha PSC, Wahab RA, Garcia-Aloy M, Madrid-Gambin F, Estruel-Amades S, Watzl B, Andrés-Lacueva C, Brennan L. Biomarkers of legume intake in human intervention and observational studies: a systematic review. GENES AND NUTRITION 2018; 13:25. [PMID: 30214640 PMCID: PMC6131749 DOI: 10.1186/s12263-018-0614-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 08/14/2018] [Indexed: 11/10/2022]
Abstract
There is a growing interest in assessing dietary intake more accurately across different population groups, and biomarkers have emerged as a complementary tool to replace traditional dietary assessment methods. The purpose of this study was to conduct a systematic review of the literature available and evaluate the applicability and validity of biomarkers of legume intake reported across various observational and intervention studies. A systematic search in PubMed, Scopus, and ISI Web of Knowledge identified 44 studies which met the inclusion criteria for the review. Results from observational studies focused on soy or soy-based foods and demonstrated positive correlations between soy intake and urinary, plasma or serum isoflavonoid levels in different population groups. Similarly, intervention studies demonstrated increased genistein and daidzein levels in urine and plasma following soy intake. Both genistein and daidzein exhibited dose-response relationships. Other isoflavonoid levels such as O-desmethylangolensin (O-DMA) and equol were also reported to increase following soy consumption. Using a developed scoring system, genistein and daidzein can be considered as promising candidate markers for soy consumption. Furthermore, genistein and daidzein also served as good estimates of soy intake as evidenced from long-term exposure studies marking their status as validated biomarkers. On the contrary, only few studies indicated proposed biomarkers for pulses intake, with pipecolic acid and S-methylcysteine reported as markers reflecting dry bean consumption, unsaturated aliphatic, hydroxyl-dicarboxylic acid related to green beans intake and trigonelline reported as marker of peas consumption. However, data regarding criteria such as specificity, dose-response and time-response relationship, reliability, and feasibility to evaluate the validity of these markers is lacking. In conclusion, despite many studies suggesting proposed biomarkers for soy, there is a lack of information on markers of other different subtypes of legumes. Further discovery and validation studies are needed in order to identify reliable biomarkers of legume intake.
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Affiliation(s)
- Pedapati S C Sri Harsha
- 1UCD School of Agriculture and Food Science, UCD Institute of Food and Health, UCD, Belfield, Dublin 4, Ireland
| | - Roshaida Abdul Wahab
- 1UCD School of Agriculture and Food Science, UCD Institute of Food and Health, UCD, Belfield, Dublin 4, Ireland
| | - Mar Garcia-Aloy
- 2Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain.,3CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Francisco Madrid-Gambin
- 2Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain.,3CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Sheila Estruel-Amades
- 2Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
| | - Bernhard Watzl
- 4Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, Germany
| | - Cristina Andrés-Lacueva
- 2Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain.,3CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Lorraine Brennan
- 1UCD School of Agriculture and Food Science, UCD Institute of Food and Health, UCD, Belfield, Dublin 4, Ireland
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Current and future perspectives of functional metabolomics in disease studies-A review. Anal Chim Acta 2018; 1037:41-54. [PMID: 30292314 DOI: 10.1016/j.aca.2018.04.006] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 03/20/2018] [Accepted: 04/13/2018] [Indexed: 12/16/2022]
Abstract
Functional metabolomics is a new concept, which studies the functions of metabolites and related enzymes focused on metabolomics. It overcomes the shortcomings of traditional discovery metabolomics of mainly relying on literatures for biological interpretation. Functional metabolomics has many advantages. Firstly, the functional roles of metabolites and related metabolic enzymes are focused. Secondly, the in vivo and in vitro experiments are conducted to validate the metabolomics findings, therefore, increasing the reliability of metabolomics study and producing the new knowledge. Thirdly, functional metabolomics can be used by biologists to investigate functions of metabolites, and related genes and proteins. In this review, we summarize the analytical, biological and clinical platforms used in functional metabolomics studies. Recent progresses of functional metabolomics in cancer, metabolic diseases and biological phenotyping are reviewed, and future development is also predicted. Because of the tremendous advantages of functional metabolomics, it will have a bright future.
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Posma JM, Garcia-Perez I, Ebbels TMD, Lindon JC, Stamler J, Elliott P, Holmes E, Nicholson JK. Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data. J Proteome Res 2018; 17:1586-1595. [PMID: 29457906 PMCID: PMC5891819 DOI: 10.1021/acs.jproteome.7b00879] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.
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Affiliation(s)
| | - Isabel Garcia-Perez
- Investigative Medicine, Department of Medicine, Faculty of Medicine , Imperial College London , W12 0NN London , United Kingdom
| | | | | | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine , Northwestern University , Chicago , Illinois 60611 , United States
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Abstract
By the turn of the twenty-first century, the use of nutraceuticals became increasingly popular in both humans and animals due to their easy access, cost-effectiveness, and tolerability with a wide margin of safety. While some nutraceuticals are safe, others have a toxic potential. For a large number of nutraceuticals, no toxicity/safety data are available due to a lack of pharmacological/toxicological studies. The safety of some nutraceuticals can be compromised via contamination with toxic plants, metals, mycotoxins, pesticides, fertilizers, drugs of abuse, etc. Knowledge of pharmacokinetic/toxicokinetic studies appears to play a pivotal role in safety and toxicity assessment of nutraceuticals. Interaction studies are essential to determine efficacy, safety, and toxicity when nutraceuticals and therapeutic drugs are used concomitantly. This chapter describes various aspects of nutraceuticals, particularly their toxic potential, and the factors influencing their safety.
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Hoijemberg PA, Pelczer I. Fast Metabolite Identification in Nuclear Magnetic Resonance Metabolomic Studies: Statistical Peak Sorting and Peak Overlap Detection for More Reliable Database Queries. J Proteome Res 2017; 17:392-401. [PMID: 29135266 DOI: 10.1021/acs.jproteome.7b00617] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
A lot of time is spent by researchers in the identification of metabolites in NMR-based metabolomic studies. The usual metabolite identification starts employing public or commercial databases to match chemical shifts thought to belong to a given compound. Statistical total correlation spectroscopy (STOCSY), in use for more than a decade, speeds the process by finding statistical correlations among peaks, being able to create a better peak list as input for the database query. However, the (normally not automated) analysis becomes challenging due to the intrinsic issue of peak overlap, where correlations of more than one compound appear in the STOCSY trace. Here we present a fully automated methodology that analyzes all STOCSY traces at once (every peak is chosen as driver peak) and overcomes the peak overlap obstacle. Peak overlap detection by clustering analysis and sorting of traces (POD-CAST) first creates an overlap matrix from the STOCSY traces, then clusters the overlap traces based on their similarity and finally calculates a cumulative overlap index (COI) to account for both strong and intermediate correlations. This information is gathered in one plot to help the user identify the groups of peaks that would belong to a single molecule and perform a more reliable database query. The simultaneous examination of all traces reduces the time of analysis, compared to viewing STOCSY traces by pairs or small groups, and condenses the redundant information in the 2D STOCSY matrix into bands containing similar traces. The COI helps in the detection of overlapping peaks, which can be added to the peak list from another cross-correlated band. POD-CAST overcomes the generally overlooked and underestimated presence of overlapping peaks and it detects them to include them in the search of all compounds contributing to the peak overlap, enabling the user to accelerate the metabolite identification process with more successful database queries and searching all tentative compounds in the sample set.
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Affiliation(s)
- Pablo A Hoijemberg
- Department of Chemistry, Frick Chemistry Laboratory, Princeton University , Princeton, New Jersey 08544, United States.,NMR Group, Centro de Investigaciones en Bionanociencias, CIBION-CONICET, Polo Científico Tecnológico , 1425 Ciudad Autónoma de Buenos Aires, Argentina
| | - István Pelczer
- Department of Chemistry, Frick Chemistry Laboratory, Princeton University , Princeton, New Jersey 08544, United States
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Rodriguez-Martinez A, Posma JM, Ayala R, Harvey N, Jimenez B, Neves AL, Lindon JC, Sonomura K, Sato TA, Matsuda F, Zalloua P, Gauguier D, Nicholson JK, Dumas ME. J-Resolved 1H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis. Anal Chem 2017; 89:11405-11412. [DOI: 10.1021/acs.analchem.7b02374] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Andrea Rodriguez-Martinez
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Joram M. Posma
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Rafael Ayala
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Nikita Harvey
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Beatriz Jimenez
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Ana L. Neves
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - John C. Lindon
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Kazuhiro Sonomura
- Life
Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto 604-8511, Japan
- Center
for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Taka-Aki Sato
- Life
Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto 604-8511, Japan
- Center
for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Fumihiko Matsuda
- Center
for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Pierre Zalloua
- School
of Medicine, Lebanese American University, Beirut 1102 2801, Lebanon
| | - Dominique Gauguier
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
- Cordeliers Research
Centre, INSERM UMR_S 1138, 75006 Paris, France
| | - Jeremy K. Nicholson
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Marc-Emmanuel Dumas
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
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