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Winkvist A, Johansson I, Ellegård L, Lindqvist HM. Towards objective measurements of habitual dietary intake patterns: comparing NMR metabolomics and food frequency questionnaire data in a population-based cohort. Nutr J 2024; 23:29. [PMID: 38429740 PMCID: PMC10908051 DOI: 10.1186/s12937-024-00929-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/23/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Low-quality, non-diverse diet is a main risk factor for premature death. Accurate measurement of habitual diet is challenging and there is a need for validated objective methods. Blood metabolite patterns reflect direct or enzymatically diet-induced metabolites. Here, we aimed to evaluate associations between blood metabolite patterns and a priori and data-driven food intake patterns. METHODS 1, 895 participants in the Northern Sweden Health and Disease Study, a population-based prospective cohort study, were included. Fasting plasma samples were analyzed with 1H Nuclear Magnetic Resonance. Food intake data from a 64-item validated food frequency questionnaire were summarized into a priori Healthy Diet Score (HDS), relative Mediterranean Diet Score (rMDS) and a set of plant-based diet indices (PDI) as well as data driven clusters from latent class analyses (LCA). Orthogonal projections to latent structures (OPLS) were used to explore clustering patterns of metabolites and their relation to reported dietary intake patterns. RESULTS Age, sex, body mass index, education and year of study participation had significant influence on OPLS metabolite models. OPLS models for healthful PDI and LCA-clusters were not significant, whereas for HDS, rMDS, PDI and unhealthful PDI significant models were obtained (CV-ANOVA p < 0.001). Still, model statistics were weak and the ability of the models to correctly classify participants into highest and lowest quartiles of rMDS, PDI and unhealthful PDI was poor (50%/78%, 42%/75% and 59%/70%, respectively). CONCLUSION Associations between blood metabolite patterns and a priori as well as data-driven food intake patterns were poor. NMR metabolomics may not be sufficiently sensitive to small metabolites that distinguish between complex dietary intake patterns, like lipids.
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Affiliation(s)
- Anna Winkvist
- Department of Internal Medicine and Clinical Nutrition, the Sahlgrenska Academy, University of Gothenburg, Box 459, Gothenburg, SE-405 30, Sweden.
- Department of Public Health and Clinical Medicine, Sustainable Health, Umeå University, Umeå, Sweden.
| | | | - Lars Ellegård
- Department of Internal Medicine and Clinical Nutrition, the Sahlgrenska Academy, University of Gothenburg, Box 459, Gothenburg, SE-405 30, Sweden
- Clinical Nutrition Unit, Department of Gastroenterology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Helen M Lindqvist
- Department of Internal Medicine and Clinical Nutrition, the Sahlgrenska Academy, University of Gothenburg, Box 459, Gothenburg, SE-405 30, Sweden
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2
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Pomary PK, Eichau S, Amigó N, Barrios L, Matesanz F, García-Valdecasas M, Hrom I, García Sánchez MI, Garcia-Martin ML. Multifaceted Analysis of Cerebrospinal Fluid and Serum from Progressive Multiple Sclerosis Patients: Potential Role of Vitamin C and Metal Ion Imbalance in the Divergence of Primary Progressive Multiple Sclerosis and Secondary Progressive Multiple Sclerosis. J Proteome Res 2023; 22:743-757. [PMID: 36720471 PMCID: PMC9990127 DOI: 10.1021/acs.jproteome.2c00460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The progressive forms of multiple sclerosis (MS) primary progressive MS (PPMS) and secondary progressive MS (SPMS) are clinically distinguished by the rate at which symptoms worsen. Little is however known about the pathological mechanisms underlying the differential rate of accumulation of pathological changes. In this study, 1H NMR spectroscopy was used to measure low-molecular-weight metabolites in paired cerebrospinal fluid (CSF) and serum of PPMS, SPMS, and control patients, as well as to determine lipoproteins and glycoproteins in serum samples. Additionally, neurodegenerative and inflammatory markers, neurofilament light (NFL) and chitinase-3-like protein 1 (CHI3L1), and the concentration of seven metal elements, Mg, Mn, Cu, Fe, Pb, Zn, and Ca, were also determined in both CSF and serum. The results indicate that the pathological changes associated with progressive MS are mainly localized in the central nervous system (CNS). More so, PPMS and SPMS patients with comparable disability status are pathologically similar in relation to neurodegeneration, neuroinflammation, and some metabolites that distinguish them from controls. However, the rapid progression of PPMS from the onset may be driven by a combination of neurotoxicity induced by heavy metals coupled with diminished CNS antioxidative capacity associated with differential intrathecal ascorbate retention and imbalance of Mg and Cu.
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Affiliation(s)
- Precious Kwadzo Pomary
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA Plataforma BIONAND), Universidad de Málaga, C/Severo Ochoa, 35, 29590 Málaga, Spain
| | - Sara Eichau
- Unidad de Neurología, Hospital Universitario Virgen de la Macarena, Av. Dr. Fedriani, 3, 41009 Sevilla, Spain
| | - Núria Amigó
- Biosfer Teslab, 43201 Reus, Spain.,Department of Basic Medical Sciences, University Rovira I Virgili, IISPV, CIBERDEM, 43201 Reus, Spain
| | - Laura Barrios
- Statistics Department, Computing Center (SGAI-CSIC), Pinar 19, Madrid 28006, Spain
| | - Fuencisla Matesanz
- Instituto de Parasitologia y Biomedicina ″Lopez-Neyra″, Avda. del Conocimiento 17. P. T. Ciencias de la Salud, 18016 Granada, Spain
| | - Marta García-Valdecasas
- Unidad de Neurología, Hospital Universitario Virgen de la Macarena, Av. Dr. Fedriani, 3, 41009 Sevilla, Spain
| | - Ioana Hrom
- Unidad de Neurología, Hospital Universitario Virgen de la Macarena, Av. Dr. Fedriani, 3, 41009 Sevilla, Spain
| | - María Isabel García Sánchez
- Unidad de Neurología, Hospital Universitario Virgen de la Macarena, Av. Dr. Fedriani, 3, 41009 Sevilla, Spain
| | - Maria Luisa Garcia-Martin
- Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina (IBIMA Plataforma BIONAND), Universidad de Málaga, C/Severo Ochoa, 35, 29590 Málaga, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials & Nanomedicine (CIBER-BBN), 29590 Málaga, Spain
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3
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Du H, Gu X, Chen J, Bai C, Duan X, Hu K. GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H- 13C HSQC-Based Metabolomics. Anal Chem 2023; 95:3195-3203. [PMID: 36728684 DOI: 10.1021/acs.analchem.2c03323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.
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Affiliation(s)
- Huan Du
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Xiu Gu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Jialuo Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Caihong Bai
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Xiaohui Duan
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Kaifeng Hu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
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4
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Judge MT, Ebbels TMD. Problems, principles and progress in computational annotation of NMR metabolomics data. Metabolomics 2022; 18:102. [PMID: 36469142 PMCID: PMC9722819 DOI: 10.1007/s11306-022-01962-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/18/2022] [Indexed: 12/08/2022]
Abstract
BACKGROUND Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for 1H 1-dimensional (1H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards. AIM OF REVIEW This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions. KEY SCIENTIFIC CONCEPTS OF REVIEW We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.
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Affiliation(s)
- Michael T Judge
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, 131 Sir Alexander Fleming Building, South Kensington Campus, London, UK
| | - Timothy M D Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, 131 Sir Alexander Fleming Building, South Kensington Campus, London, UK.
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5
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Martin WP, Nair M, Chuah YH, Malmodin D, Pedersen A, Abrahamsson S, Hutter M, Abdelaal M, Elliott JA, Fearon N, Eckhardt H, Godson C, Brennan EP, Fändriks L, le Roux CW, Docherty NG. Dietary restriction and medical therapy drives PPARα-regulated improvements in early diabetic kidney disease in male rats. Clin Sci (Lond) 2022; 136:1485-1511. [PMID: 36259366 PMCID: PMC7613831 DOI: 10.1042/cs20220205] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 10/14/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022]
Abstract
The attenuation of diabetic kidney disease (DKD) by metabolic surgery is enhanced by pharmacotherapy promoting renal fatty acid oxidation (FAO). Using the Zucker Diabetic Fatty and Zucker Diabetic Sprague Dawley rat models of DKD, we conducted studies to determine if these effects could be replicated with a non-invasive bariatric mimetic intervention. Metabolic control and renal injury were compared in rats undergoing a dietary restriction plus medical therapy protocol (DMT; fenofibrate, liraglutide, metformin, ramipril, and rosuvastatin) and ad libitum-fed controls. The global renal cortical transcriptome and urinary 1H-NMR metabolomic profiles were also compared. Kidney cell type-specific and medication-specific transcriptomic responses were explored through in silico deconvolution. Transcriptomic and metabolomic correlates of improvements in kidney structure were defined using a molecular morphometric approach. The DMT protocol led to ∼20% weight loss, normalized metabolic parameters and was associated with reductions in indices of glomerular and proximal tubular injury. The transcriptomic response to DMT was dominated by changes in fenofibrate- and peroxisome proliferator-activated receptor-α (PPARα)-governed peroxisomal and mitochondrial FAO transcripts localizing to the proximal tubule. DMT induced urinary excretion of PPARα-regulated metabolites involved in nicotinamide metabolism and reversed DKD-associated changes in the urinary excretion of tricarboxylic acid (TCA) cycle intermediates. FAO transcripts and urinary nicotinamide and TCA cycle metabolites were moderately to strongly correlated with improvements in glomerular and proximal tubular injury. Weight loss plus pharmacological PPARα agonism is a promising means of attenuating DKD.
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Affiliation(s)
- William P. Martin
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Meera Nair
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Yeong H.D. Chuah
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Daniel Malmodin
- Swedish NMR Centre, University of Gothenburg, 40530 Gothenburg, Sweden
| | - Anders Pedersen
- Swedish NMR Centre, University of Gothenburg, 40530 Gothenburg, Sweden
| | - Sanna Abrahamsson
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
| | - Michaela Hutter
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Mahmoud Abdelaal
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Jessie A. Elliott
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Naomi Fearon
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Hans Eckhardt
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Catherine Godson
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Eoin P. Brennan
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
| | - Lars Fändriks
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
| | - Carel W. le Roux
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- Diabetes Research Group, Ulster University, Coleraine BT52 1SA, UK
| | - Neil G. Docherty
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
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6
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Waschina S, Seeger K. Using in-tube extraction and slice selective NMR experiments allow imaging via statistical analysis of metabolic profiles. Anal Chim Acta 2022; 1231:340419. [DOI: 10.1016/j.aca.2022.340419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/19/2022] [Accepted: 09/17/2022] [Indexed: 11/25/2022]
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7
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Bliziotis NG, Kluijtmans LAJ, Tinnevelt GH, Reel P, Reel S, Langton K, Robledo M, Pamporaki C, Pecori A, Van Kralingen J, Tetti M, Engelke UFH, Erlic Z, Engel J, Deutschbein T, Nölting S, Prejbisz A, Richter S, Adamski J, Januszewicz A, Ceccato F, Scaroni C, Dennedy MC, Williams TA, Lenzini L, Gimenez-Roqueplo AP, Davies E, Fassnacht M, Remde H, Eisenhofer G, Beuschlein F, Kroiss M, Jefferson E, Zennaro MC, Wevers RA, Jansen JJ, Deinum J, Timmers HJLM. Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension. Metabolites 2022; 12:679. [PMID: 35893246 PMCID: PMC9394285 DOI: 10.3390/metabo12080679] [Citation(s) in RCA: 1] [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: 05/30/2022] [Revised: 06/17/2022] [Accepted: 07/11/2022] [Indexed: 11/24/2022] Open
Abstract
Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.
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Affiliation(s)
- Nikolaos G. Bliziotis
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Leo A. J. Kluijtmans
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Gerjen H. Tinnevelt
- Department of Analytical Chemistry, Institute for Molecules and Materials, Radboud University, 6500 HB Nijmegen, The Netherlands; (G.H.T.); (J.J.J.)
| | - Parminder Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee DD2 4BF, UK; (P.R.); (S.R.); (E.J.)
| | - Smarti Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee DD2 4BF, UK; (P.R.); (S.R.); (E.J.)
| | - Katharina Langton
- Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (K.L.); (C.P.); (G.E.)
| | - Mercedes Robledo
- Hereditary Endocrine Cancer Group, Spanish National Cancer Research Centre (CNIO), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 28029 Madrid, Spain;
| | - Christina Pamporaki
- Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (K.L.); (C.P.); (G.E.)
| | - Alessio Pecori
- Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, 10124 Torino, Italy; (A.P.); (M.T.); (T.A.W.)
| | - Josie Van Kralingen
- British Heart Foundation Glasgow Cardiovascular Research Centre (BHF GCRC), Institute of Cardiovascular & Medical Sciences (ICAMS), University of Glasgow, Glasgow G12 8TA, UK; (J.V.K.); (E.D.)
| | - Martina Tetti
- Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, 10124 Torino, Italy; (A.P.); (M.T.); (T.A.W.)
| | - Udo F. H. Engelke
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Zoran Erlic
- Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich (USZ), University of Zurich (UZH), 8006 Zurich, Switzerland; (Z.E.); (F.B.)
| | - Jasper Engel
- Biometris, Wageningen University & Research, 6708 PB Wageningen, The Netherlands;
| | - Timo Deutschbein
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, 97080 Würzburg, Germany; (T.D.); (M.F.); (H.R.); (M.K.)
- Medicover Oldenburg MVZ, 26122 Oldenburg, Germany
| | - Svenja Nölting
- Department of Medicine IV, University Hospital, LMU Munich, 80336 Munich, Germany;
| | - Aleksander Prejbisz
- Department of Hypertension, Institute of Cardiology, 04-628 Warsaw, Poland; (A.P.); (A.J.)
| | - Susan Richter
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, 01307 Dresden, Germany;
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Genome Analysis Center, Helmholtz Center München, German Research Center for Environmental Health, 85764 Neuherberg, Germany;
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia
- Institute of Experimental Genetics, Technical University München, 85350 Freising-Weihenstephan, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 119077 Singapore, Singapore
| | - Andrzej Januszewicz
- Department of Hypertension, Institute of Cardiology, 04-628 Warsaw, Poland; (A.P.); (A.J.)
| | - Filippo Ceccato
- Endocrinology Unit, Department of Medicine DIMED, University-Hospital of Padova, 35128 Padova, Italy; (F.C.); (C.S.)
| | - Carla Scaroni
- Endocrinology Unit, Department of Medicine DIMED, University-Hospital of Padova, 35128 Padova, Italy; (F.C.); (C.S.)
| | - Michael C. Dennedy
- The Discipline of Pharmacology and Therapeutics, School of Medicine, National University of Ireland, H91 CF50 Galway, Ireland;
| | - Tracy A. Williams
- Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Torino, 10124 Torino, Italy; (A.P.); (M.T.); (T.A.W.)
| | - Livia Lenzini
- Department of Medicine-DIMED, Emergency and Hypertension Unit, University of Padova, University Hospital, 35126 Padova, Italy;
| | - Anne-Paule Gimenez-Roqueplo
- INSERM, PARCC, Université de Paris, 75015 Paris, France; (A.-P.G.-R.); (M.-C.Z.)
- Service de Genétique, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 75015 Paris, France
| | - Eleanor Davies
- British Heart Foundation Glasgow Cardiovascular Research Centre (BHF GCRC), Institute of Cardiovascular & Medical Sciences (ICAMS), University of Glasgow, Glasgow G12 8TA, UK; (J.V.K.); (E.D.)
| | - Martin Fassnacht
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, 97080 Würzburg, Germany; (T.D.); (M.F.); (H.R.); (M.K.)
- Core Unit Clinical Mass Spectrometry, University Hospital Würzburg, 97080 Würzburg, Germany
- Comprehensive Cancer Center Mainfranken, Würzburg University, 97070 Würzburg, Germany
| | - Hanna Remde
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, 97080 Würzburg, Germany; (T.D.); (M.F.); (H.R.); (M.K.)
| | - Graeme Eisenhofer
- Department of Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (K.L.); (C.P.); (G.E.)
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, 01307 Dresden, Germany;
| | - Felix Beuschlein
- Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich (USZ), University of Zurich (UZH), 8006 Zurich, Switzerland; (Z.E.); (F.B.)
- Department of Medicine IV, University Hospital, LMU Munich, 80336 Munich, Germany;
| | - Matthias Kroiss
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, 97080 Würzburg, Germany; (T.D.); (M.F.); (H.R.); (M.K.)
- Department of Medicine IV, University Hospital, LMU Munich, 80336 Munich, Germany;
- Core Unit Clinical Mass Spectrometry, University Hospital Würzburg, 97080 Würzburg, Germany
- Comprehensive Cancer Center Mainfranken, Würzburg University, 97070 Würzburg, Germany
| | - Emily Jefferson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee DD2 4BF, UK; (P.R.); (S.R.); (E.J.)
- Institute of Health & Wellbeing, Glasgow University, Glasgow G12 8RZ, UK
| | - Maria-Christina Zennaro
- INSERM, PARCC, Université de Paris, 75015 Paris, France; (A.-P.G.-R.); (M.-C.Z.)
- Service de Genétique, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, 75015 Paris, France
| | - Ron A. Wevers
- Department of Laboratory Medicine, Translational Metabolic Laboratory, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Jeroen J. Jansen
- Department of Analytical Chemistry, Institute for Molecules and Materials, Radboud University, 6500 HB Nijmegen, The Netherlands; (G.H.T.); (J.J.J.)
| | - Jaap Deinum
- Department of Internal Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
| | - Henri J. L. M. Timmers
- Department of Internal Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands;
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Magalhães S, Almeida I, Pereira CD, Rebelo S, Goodfellow BJ, Nunes A. The Long-Term Culture of Human Fibroblasts Reveals a Spectroscopic Signature of Senescence. Int J Mol Sci 2022; 23:ijms23105830. [PMID: 35628639 PMCID: PMC9146002 DOI: 10.3390/ijms23105830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/16/2022] [Accepted: 05/17/2022] [Indexed: 11/24/2022] Open
Abstract
Aging is a complex process which leads to progressive loss of fitness/capability/ability, increasing susceptibility to disease and, ultimately, death. Regardless of the organism, there are some features common to aging, namely, the loss of proteostasis and cell senescence. Mammalian cell lines have been used as models to study the aging process, in particular, cell senescence. Thus, the aim of this study was to characterize the senescence-associated metabolic profile of a long-term culture of human fibroblasts using Fourier Transform Infrared and Nuclear Magnetic Resonance spectroscopy. We sub-cultivated fibroblasts from a newborn donor from passage 4 to passage 17 and the results showed deep changes in the spectroscopic profile of cells over time. Late passage cells were characterized by a decrease in the length of fatty acid chains, triglycerides and cholesterol and an increase in lipid unsaturation. We also found an increase in the content of intermolecular β-sheets, possibly indicating an increase in protein aggregation levels in cells of later passages. Metabolic profiling by NMR showed increased levels of extracellular lactate, phosphocholine and glycine in cells at later passages. This study suggests that spectroscopy approaches can be successfully used to study changes concomitant with cell senescence and validate the use of human fibroblasts as a model to monitor the aging process.
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Affiliation(s)
- Sandra Magalhães
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
- CICECO—Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;
| | - Idália Almeida
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
- CICECO—Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;
| | - Cátia D. Pereira
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
| | - Sandra Rebelo
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
| | - Brian J. Goodfellow
- CICECO—Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;
| | - Alexandra Nunes
- iBiMED—Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Agra do Crasto, 3810-193 Aveiro, Portugal; (S.M.); (I.A.); (C.D.P.); (S.R.)
- Correspondence: ; Tel.: +351-234-324-435
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9
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Lemos ÁT, Goodfellow BJ, Delgadillo I, Saraiva JA. NMR metabolic composition profiling of high pressure pasteurized milk preserved by hyperbaric storage at room temperature. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108660] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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10
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Martin WP, Malmodin D, Pedersen A, Wallace M, Fändriks L, Aboud CM, Petry TBZ, Cunha da Silveira LP, da Costa Silva ACC, Cohen RV, le Roux CW, Docherty NG. Urinary Metabolomic Changes Accompanying Albuminuria Remission following Gastric Bypass Surgery for Type 2 Diabetic Kidney Disease. Metabolites 2022; 12:139. [PMID: 35186675 PMCID: PMC7612403 DOI: 10.3390/metabo12020139] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
In the Microvascular Outcomes after Metabolic Surgery randomised clinical trial (MOMS RCT, NCT01821508), combined metabolic surgery (gastric bypass) plus medical therapy (CSM) was superior to medical therapy alone (MTA) as a means of achieving albuminuria remission at 2-year follow-up in patients with obesity and early diabetic kidney disease (DKD). In the present study, we assessed the urinary 1H-NMR metabolome in a subgroup of patients from both arms of the MOMS RCT at baseline and 6-month follow-up. Whilst CSM and MTA both reduced the urinary excretion of sugars, CSM generated a distinctive urinary metabolomic profile characterised by increases in host–microbial co-metabolites (N-phenylacetylglycine, trimethylamine N-oxide, and 4-aminobutyrate (GABA)) and amino acids (arginine and glutamine). Furthermore, reductions in aromatic amino acids (phenylalanine and tyrosine), as well as branched-chain amino acids (BCAAs) and related catabolites (valine, leucine, 3-hydroxyisobutyrate, 3-hydroxyisovalerate, and 3-methyl-2-oxovalerate), were observed following CSM but not MTA. Improvements in BMI did not correlate with improvements in metabolic and renal indices following CSM. Conversely, urinary metabolites changed by CSM at 6 months were moderately to strongly correlated with improvements in blood pressure, glycaemia, triglycerides, and albuminuria up to 24 months following treatment initiation, highlighting the potential involvement of these shifts in the urinary metabolomic profile in the metabolic and renoprotective effects of CSM.
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Affiliation(s)
- William P. Martin
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland; (W.P.M.); (C.W.l.R.)
| | - Daniel Malmodin
- Swedish NMR Centre, University of Gothenburg, 40530 Gothenburg, Sweden; (D.M.); (A.P.)
| | - Anders Pedersen
- Swedish NMR Centre, University of Gothenburg, 40530 Gothenburg, Sweden; (D.M.); (A.P.)
| | - Martina Wallace
- Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland;
| | - Lars Fändriks
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden;
| | - Cristina M. Aboud
- The Centre for Obesity and Diabetes, Oswaldo Cruz German Hospital, São Paulo 01333-010, Brazil; (C.M.A.); (T.B.Z.P.); (L.P.C.d.S.); (A.C.C.d.C.S.); (R.V.C.)
| | - Tarissa B. Zanata Petry
- The Centre for Obesity and Diabetes, Oswaldo Cruz German Hospital, São Paulo 01333-010, Brazil; (C.M.A.); (T.B.Z.P.); (L.P.C.d.S.); (A.C.C.d.C.S.); (R.V.C.)
| | - Lívia P. Cunha da Silveira
- The Centre for Obesity and Diabetes, Oswaldo Cruz German Hospital, São Paulo 01333-010, Brazil; (C.M.A.); (T.B.Z.P.); (L.P.C.d.S.); (A.C.C.d.C.S.); (R.V.C.)
| | - Ana C. Calmon da Costa Silva
- The Centre for Obesity and Diabetes, Oswaldo Cruz German Hospital, São Paulo 01333-010, Brazil; (C.M.A.); (T.B.Z.P.); (L.P.C.d.S.); (A.C.C.d.C.S.); (R.V.C.)
| | - Ricardo V. Cohen
- The Centre for Obesity and Diabetes, Oswaldo Cruz German Hospital, São Paulo 01333-010, Brazil; (C.M.A.); (T.B.Z.P.); (L.P.C.d.S.); (A.C.C.d.C.S.); (R.V.C.)
| | - Carel W. le Roux
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland; (W.P.M.); (C.W.l.R.)
- Diabetes Research Group, Ulster University, Coleraine BT52 1SA, UK
| | - Neil G. Docherty
- Diabetes Complications Research Centre, School of Medicine, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland; (W.P.M.); (C.W.l.R.)
- Correspondence:
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11
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Ehlers M, Horn B, Raeke J, Fauhl-Hassek C, Hermann A, Brockmeyer J, Riedl J. Towards harmonization of non-targeted 1H NMR spectroscopy-based wine authentication: Instrument comparison. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108508] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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12
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Martin WP, Chuah YHD, Abdelaal M, Pedersen A, Malmodin D, Abrahamsson S, Hutter M, Godson C, Brennan EP, Fändriks L, le Roux CW, Docherty NG. Medications Activating Tubular Fatty Acid Oxidation Enhance the Protective Effects of Roux-en-Y Gastric Bypass Surgery in a Rat Model of Early Diabetic Kidney Disease. Front Endocrinol (Lausanne) 2022; 12:757228. [PMID: 35222262 PMCID: PMC8867227 DOI: 10.3389/fendo.2021.757228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 12/23/2021] [Indexed: 01/03/2023] Open
Abstract
Background Roux-en-Y gastric bypass surgery (RYGB) improves biochemical and histological parameters of diabetic kidney disease (DKD). Targeted adjunct medical therapy may enhance renoprotection following RYGB. Methods The effects of RYGB and RYGB plus fenofibrate, metformin, ramipril, and rosuvastatin (RYGB-FMRR) on metabolic control and histological and ultrastructural indices of glomerular and proximal tubular injury were compared in the Zucker Diabetic Sprague Dawley (ZDSD) rat model of DKD. Renal cortical transcriptomic (RNA-sequencing) and urinary metabolomic (1H-NMR spectroscopy) responses were profiled and integrated. Transcripts were assigned to kidney cell types through in silico deconvolution in kidney single-nucleus RNA-sequencing and microdissected tubular epithelial cell proteomics datasets. Medication-specific transcriptomic responses following RYGB-FMRR were explored using a network pharmacology approach. Omic correlates of improvements in structural and ultrastructural indices of renal injury were defined using a molecular morphometric approach. Results RYGB-FMRR was superior to RYGB alone with respect to metabolic control, albuminuria, and histological and ultrastructural indices of glomerular injury. RYGB-FMRR reversed DKD-associated changes in mitochondrial morphology in the proximal tubule to a greater extent than RYGB. Attenuation of transcriptomic pathway level activation of pro-fibrotic responses was greater after RYGB-FMRR than RYGB. Fenofibrate was found to be the principal medication effector of gene expression changes following RYGB-FMRR, which led to the transcriptional induction of PPARα-regulated genes that are predominantly expressed in the proximal tubule and which regulate peroxisomal and mitochondrial fatty acid oxidation (FAO). After omics integration, expression of these FAO transcripts positively correlated with urinary levels of PPARα-regulated nicotinamide metabolites and negatively correlated with urinary tricarboxylic acid (TCA) cycle intermediates. Changes in FAO transcripts and nicotinamide and TCA cycle metabolites following RYGB-FMRR correlated strongly with improvements in glomerular and proximal tubular injury. Conclusions Integrative multi-omic analyses point to PPARα-stimulated FAO in the proximal tubule as a dominant effector of treatment response to combined surgical and medical therapy in experimental DKD. Synergism between RYGB and pharmacological stimulation of FAO represents a promising combinatorial approach to the treatment of DKD in the setting of obesity.
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Affiliation(s)
- William P. Martin
- Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland
| | - Yeong H. D. Chuah
- Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland
| | - Mahmoud Abdelaal
- Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland
| | - Anders Pedersen
- Swedish NMR Centre, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Malmodin
- Swedish NMR Centre, University of Gothenburg, Gothenburg, Sweden
| | - Sanna Abrahamsson
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Michaela Hutter
- Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland
| | - Catherine Godson
- Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland
| | - Eoin P. Brennan
- Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland
| | - Lars Fändriks
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Carel W. le Roux
- Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland
- Diabetes Research Group, Ulster University, Coleraine, United Kingdom
| | - Neil G. Docherty
- Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Dublin, Ireland
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13
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MacDonald R, Sokolenko S. Detection of highly overlapping peaks via adaptive apodization. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 333:107104. [PMID: 34801821 DOI: 10.1016/j.jmr.2021.107104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/20/2021] [Accepted: 11/02/2021] [Indexed: 06/13/2023]
Abstract
Accurate peak detection is an essential component of many NMR tasks such as peak alignment, compound identification, and global spectral deconvolution. However, current peak detection approaches are generally limited by their ability to deal with spectral overlap, which has a deleterious effect on downstream data processing. In this work, we present the use of an adaptive apodization strategy that improves the detection of highly overlapping peaks. Sensitivity enhancement is used to identify general regions of interest and resolution enhancement is used to separate overlapping peaks, with parameters for both calculated directly from the data. Further limits on peak width help to reduce false positives. The method proposed in this work has been implemented in an open-source R package called rnmrfind that is available for download on GitHub (https://github.com/ssokolen/rnmrfind). A set of default parameters have been chosen to provide effective peak detection while keeping false positives to a minimum; however, application-specific tuning is possible through the modification of minimum peak width at half height (in Hz) and noise cutoff threshold (as a factor of estimated standard deviation). Comparison to existing packages rNMR and speaq on a series of 1H NMR spectra demonstrates improved peak resolution with little to no apparent drawbacks.
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Affiliation(s)
- Ruis MacDonald
- Process Engineering and Applied Science, Dalhousie University, Sexton Campus, 5273 DaCosta Row, PO Box 15000, Halifax NS B3H 4R2, Canada
| | - Stanislav Sokolenko
- Process Engineering and Applied Science, Dalhousie University, Sexton Campus, 5273 DaCosta Row, PO Box 15000, Halifax NS B3H 4R2, Canada.
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14
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Letertre MPM, Giraudeau P, de Tullio P. Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives. Front Mol Biosci 2021; 8:698337. [PMID: 34616770 PMCID: PMC8488110 DOI: 10.3389/fmolb.2021.698337] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.
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Affiliation(s)
| | | | - Pascal de Tullio
- Metabolomics Group, Center for Interdisciplinary Research of Medicine (CIRM), Department of Pharmacy, Université de Liège, Liège, Belgique
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15
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Wallenius V, Elebring E, Casselbrant A, Laurenius A, le Roux CW, Docherty NG, Biörserud C, Björnfot N, Engström M, Marschall HU, Fändriks L. Glycemic Control and Metabolic Adaptation in Response to High-Fat versus High-Carbohydrate Diets-Data from a Randomized Cross-Over Study in Healthy Subjects. Nutrients 2021; 13:nu13103322. [PMID: 34684324 PMCID: PMC8538379 DOI: 10.3390/nu13103322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/13/2021] [Accepted: 09/20/2021] [Indexed: 11/26/2022] Open
Abstract
Granular study of metabolic responses to alterations in the ratio of dietary macro-nutrients can enhance our understanding of how dietary modifications influence patients with impaired glycemic control. In order to study the effect of diets enriched in fat or carbohydrates, fifteen healthy, normal-weight volunteers received, in a cross-over design, and in a randomized unblinded order, two weeks of an iso-caloric high-fat diet (HFD: 60E% from fat) and a high-carbohydrate diet (HCD: 60E% from carbohydrates). A mixed meal test (MMT) was performed at the end of each dietary period to examine glucose clearance kinetics and insulin and incretin hormone levels, as well as plasma metabolomic profiles. The MMT induced almost identical glycemia and insulinemia following the HFD or HCD. GLP-1 levels were higher after the HFD vs. HCD, whereas GIP did not differ. The HFD, compared to the HCD, increased the levels of several metabolomic markers of risk for the development of insulin resistance, e.g., branched-chain amino acid (valine and leucine), creatine and α-hydroxybutyric acid levels. In normal-weight, healthy volunteers, two weeks of the HFD vs. HCD showed similar profiles of meal-induced glycemia and insulinemia. Despite this, the HFD showed a metabolomic pattern implying a risk for a metabolic shift towards impaired insulin sensitivity in the long run.
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Affiliation(s)
- Ville Wallenius
- Institute of Clinical Sciences, Department Surgery, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden; (E.E.); (A.C.); (A.L.); (C.B.); (N.B.); (M.E.); (L.F.)
- Correspondence: ; Tel.: +46-733836749
| | - Erik Elebring
- Institute of Clinical Sciences, Department Surgery, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden; (E.E.); (A.C.); (A.L.); (C.B.); (N.B.); (M.E.); (L.F.)
| | - Anna Casselbrant
- Institute of Clinical Sciences, Department Surgery, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden; (E.E.); (A.C.); (A.L.); (C.B.); (N.B.); (M.E.); (L.F.)
| | - Anna Laurenius
- Institute of Clinical Sciences, Department Surgery, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden; (E.E.); (A.C.); (A.L.); (C.B.); (N.B.); (M.E.); (L.F.)
| | - Carel W. le Roux
- Metabolic Medicine, School of Medicine, Conway Institute, University College Dublin, Dublin 4, Ireland; (C.W.l.R.); (N.G.D.)
| | - Neil G. Docherty
- Metabolic Medicine, School of Medicine, Conway Institute, University College Dublin, Dublin 4, Ireland; (C.W.l.R.); (N.G.D.)
| | - Christina Biörserud
- Institute of Clinical Sciences, Department Surgery, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden; (E.E.); (A.C.); (A.L.); (C.B.); (N.B.); (M.E.); (L.F.)
| | - Niclas Björnfot
- Institute of Clinical Sciences, Department Surgery, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden; (E.E.); (A.C.); (A.L.); (C.B.); (N.B.); (M.E.); (L.F.)
| | - My Engström
- Institute of Clinical Sciences, Department Surgery, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden; (E.E.); (A.C.); (A.L.); (C.B.); (N.B.); (M.E.); (L.F.)
| | - Hanns-Ulrich Marschall
- Institute of Medicine, Department Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden;
| | - Lars Fändriks
- Institute of Clinical Sciences, Department Surgery, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden; (E.E.); (A.C.); (A.L.); (C.B.); (N.B.); (M.E.); (L.F.)
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Frizzo R, Bortoletto E, Riello T, Leanza L, Schievano E, Venier P, Mammi S. NMR Metabolite Profiles of the Bivalve Mollusc Mytilus galloprovincialis Before and After Immune Stimulation With Vibrio splendidus. Front Mol Biosci 2021; 8:686770. [PMID: 34540890 PMCID: PMC8447493 DOI: 10.3389/fmolb.2021.686770] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/15/2021] [Indexed: 01/26/2023] Open
Abstract
The hemolymph metabolome of Mytilus galloprovincialis injected with live Vibrio splendidus bacteria was analyzed by 1H-NMR spectrometry. Changes in spectral hemolymph profiles were already detected after mussel acclimation (3 days at 18 or 25 °C). A significant decrease of succinic acid was accompanied by an increase of most free amino acids, mytilitol, and, to a smaller degree, osmolytes. These metabolic changes are consistent with effective osmoregulation, and the restart of aerobic respiration after the functional anaerobiosis occurred during transport. The injection of Vibrio splendidus in mussels acclimated at 18°C caused a significant decrease of several amino acids, sugars, and unassigned chemical species, more pronounced at 24 than at 12 h postinjection. Correlation heatmaps indicated dynamic metabolic adjustments and the relevance of protein turnover in maintaining the homeostasis during the response to stressful stimuli. This study confirms NMR-based metabolomics as a feasible analytical approach complementary to other omics techniques in the investigation of the functional mussel responses to environmental challenges.
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Affiliation(s)
- Riccardo Frizzo
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | | | - Tobia Riello
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Luigi Leanza
- Department of Biology, University of Padova, Padova, Italy
| | | | - Paola Venier
- Department of Biology, University of Padova, Padova, Italy
| | - Stefano Mammi
- Department of Chemical Sciences, University of Padova, Padova, Italy
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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18
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Statistical analysis in metabolic phenotyping. Nat Protoc 2021; 16:4299-4326. [PMID: 34321638 DOI: 10.1038/s41596-021-00579-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 05/27/2021] [Indexed: 01/09/2023]
Abstract
Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.
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19
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Schultheiss UT, Kosch R, Kotsis F, Altenbuchinger M, Zacharias HU. Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses. Metabolites 2021; 11:460. [PMID: 34357354 PMCID: PMC8304377 DOI: 10.3390/metabo11070460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 12/14/2022] Open
Abstract
Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.
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Affiliation(s)
- Ulla T. Schultheiss
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Robin Kosch
- Computational Biology, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Fruzsina Kotsis
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; (U.T.S.); (F.K.)
- Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Michael Altenbuchinger
- Institute of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
| | - Helena U. Zacharias
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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20
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Röhnisch HE, Eriksson J, Tran LV, Müllner E, Sandström C, Moazzami AA. Improved Automated Quantification Algorithm (AQuA) and Its Application to NMR-Based Metabolomics of EDTA-Containing Plasma. Anal Chem 2021; 93:8729-8738. [PMID: 34128648 PMCID: PMC8253485 DOI: 10.1021/acs.analchem.0c04233] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
We have recently
presented an Automated Quantification Algorithm
(AQuA) and demonstrated its utility for rapid and accurate absolute
metabolite quantification in 1H NMR spectra in which positions
and line widths of signals were predicted from a constant metabolite
spectral library. The AQuA quantifies based on one preselected signal
per metabolite and employs library spectra to model interferences
from other metabolite signals. However, for some types of spectra,
the interspectral deviations of signal positions and line widths can
be pronounced; hence, interferences cannot be modeled using a constant
spectral library. We here address this issue and present an improved
AQuA that handles interspectral deviations. The improved AQuA monitors
and characterizes the appearance of specific signals in each spectrum
and automatically adjusts the spectral library to model interferences
accordingly. The performance of the improved AQuA was tested on a
large data set from plasma samples collected using ethylenediaminetetraacetic
acid (EDTA) as an anticoagulant (n = 772). These
spectra provided a suitable test system for the improved AQuA since
EDTA signals (i) vary in intensity, position, and line width between
spectra and (ii) interfere with many signals from plasma metabolites
targeted for quantification (n = 54). Without the
improvement, ca. 20 out of the 54 metabolites would have been overestimated.
This included acetylcarnitine and ornithine, which are considered
particularly difficult to quantify with 1H NMR in EDTA-containing
plasma. Furthermore, the improved AQuA performed rapidly (<10 s
for all spectra). We believe that the improved AQuA provides a basis
for automated quantification in other data sets where specific signals
show interspectral deviations.
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Affiliation(s)
- Hanna E Röhnisch
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Jan Eriksson
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Lan V Tran
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Elisabeth Müllner
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Corine Sandström
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Ali A Moazzami
- Department of Molecular Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
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21
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Solovyev PA, Fauhl-Hassek C, Riedl J, Esslinger S, Bontempo L, Camin F. NMR spectroscopy in wine authentication: An official control perspective. Compr Rev Food Sci Food Saf 2021; 20:2040-2062. [PMID: 33506593 DOI: 10.1111/1541-4337.12700] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 11/30/2020] [Accepted: 12/23/2020] [Indexed: 12/14/2022]
Abstract
Wine authentication is vital in identifying malpractice and fraud, and various physical and chemical analytical techniques have been employed for this purpose. Besides wet chemistry, these include chromatography, isotopic ratio mass spectrometry, optical spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy, which have been applied in recent years in combination with chemometric approaches. For many years, 2 H NMR spectroscopy was the method of choice and achieved official recognition in the detection of sugar addition to grape products. Recently, 1 H NMR spectroscopy, a simpler and faster method (in terms of sample preparation), has gathered more and more attention in wine analysis, even if it still lacks official recognition. This technique makes targeted quantitative determination of wine ingredients and nontargeted detection of the metabolomic fingerprint of a wine sample possible. This review summarizes the possibilities and limitations of 1 H NMR spectroscopy in analytical wine authentication, by reviewing its applications as reported in the literature. Examples of commercial and open-source solutions combining NMR spectroscopy and chemometrics are also examined herein, together with its opportunities of becoming an official method.
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Affiliation(s)
- Pavel A Solovyev
- Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), via E. Mach 1, San Michele all'Adige, 38010, Italy
| | - Carsten Fauhl-Hassek
- German Federal Institute for Risk Assessment, Department Safety in the Food Chain, Unit Product Identity, Supply Chains and Traceability, Max-Dohrn Strasse, 8-10, Berlin, 10589, Germany
| | - Janet Riedl
- German Federal Institute for Risk Assessment, Department Safety in the Food Chain, Unit Product Identity, Supply Chains and Traceability, Max-Dohrn Strasse, 8-10, Berlin, 10589, Germany
| | - Susanne Esslinger
- German Federal Institute for Risk Assessment, Department Safety in the Food Chain, Unit Product Identity, Supply Chains and Traceability, Max-Dohrn Strasse, 8-10, Berlin, 10589, Germany
| | - Luana Bontempo
- Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), via E. Mach 1, San Michele all'Adige, 38010, Italy
| | - Federica Camin
- Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), via E. Mach 1, San Michele all'Adige, 38010, Italy.,Center Agriculture Food Environment (C3A), University of Trento, via Mach 1, San Michele all'Adige, Tennessee, 38010, Italy
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22
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Lefort G, Liaubet L, Marty-Gasset N, Canlet C, Vialaneix N, Servien R. Joint Automatic Metabolite Identification and Quantification of a Set of 1H NMR Spectra. Anal Chem 2021; 93:2861-2870. [PMID: 33497193 DOI: 10.1021/acs.analchem.0c04232] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Metabolomics is a promising approach to characterize phenotypes or to identify biomarkers. It is also easily accessible through NMR, which can provide a comprehensive understanding of the metabolome of any living organisms. However, the analysis of 1H NMR spectrum remains difficult, mainly due to the different problems encountered to perform automatic identification and quantification of metabolites in a reproducible way. In addition, methods that perform automatic identification and quantification of metabolites are often designed to process one given complex mixture spectrum at a time. Hence, when a set of complex mixture spectra coming from the same experiment has to be processed, the approach is simply repeated independently for every spectrum, despite their resemblance. Here, we present new methods that are the first to either align spectra or to identify and quantify metabolites by integrating information coming from several complex spectra of the same experiment. The performances of these new methods are then evaluated on both simulated and real datasets. The results show an improvement in the metabolite identification and in the accuracy of metabolite quantifications, especially when the concentration is low. This joint procedure is available in version 2.0 of ASICS package.
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Affiliation(s)
- Gaëlle Lefort
- Université de Toulouse, INRAE, UR MIAT, Castanet-Tolosan F-31326, France.,GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet-Tolosan F-31326, France
| | - Laurence Liaubet
- GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet-Tolosan F-31326, France
| | | | - Cécile Canlet
- INRAE, Université de Toulouse, ENVT, Toxalim, Toulouse F-31027, France.,Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, Toulouse F-31027, France
| | - Nathalie Vialaneix
- Université de Toulouse, INRAE, UR MIAT, Castanet-Tolosan F-31326, France
| | - Rémi Servien
- INRAE, Univ. Montpellier, LBE, 102 Avenue des étangs, Narbonne F-11100, France.,INTHERES, Université de Toulouse, INRAE, ENVT, Toulouse F-31027, France
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23
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Klein MS. Affine Transformation of Negative Values for NMR Metabolomics Using the mrbin R Package. J Proteome Res 2021; 20:1397-1404. [PMID: 33417772 DOI: 10.1021/acs.jproteome.0c00684] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Data from untargeted metabolomics studies employing nuclear magnetic resonance (NMR) spectroscopy oftentimes contain negative values. These negative values hamper data processing and analysis algorithms and prevent the use of such data in multiomics integration settings. New methods to deal with such negative values are thus an urgent need in the metabolomics community. This study presents affine transformation of negative values (ATNV), a novel algorithm for replacement of negative values in NMR data sets. ATNV was implemented in the R package mrbin, which features interactive menus for user-friendly application and is available for free for various operating systems within the free R statistical programming language. The novel algorithms were tested on a set of human urinary NMR spectra and were able to successfully identify relevant metabolites.
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Affiliation(s)
- Matthias S Klein
- Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Road, Columbus, Ohio 43210, United States
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24
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Edison AS, Colonna M, Gouveia GJ, Holderman NR, Judge MT, Shen X, Zhang S. NMR: Unique Strengths That Enhance Modern Metabolomics Research. Anal Chem 2020; 93:478-499. [DOI: 10.1021/acs.analchem.0c04414] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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25
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Madrid-Gambin F, Oller-Moreno S, Fernandez L, Bartova S, Giner MP, Joyce C, Ferraro F, Montoliu I, Moco S, Marco S. AlpsNMR: an R package for signal processing of fully untargeted NMR-based metabolomics. Bioinformatics 2020; 36:2943-2945. [PMID: 31930381 DOI: 10.1093/bioinformatics/btaa022] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 12/17/2019] [Accepted: 01/10/2020] [Indexed: 12/22/2022] Open
Abstract
SUMMARY Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines. AVAILABILITY AND IMPLEMENTATION The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Francisco Madrid-Gambin
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, 08028 Barcelona, Spain
| | - Sergio Oller-Moreno
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, 08028 Barcelona, Spain
| | - Luis Fernandez
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, 08028 Barcelona, Spain.,Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona 08028, Spain
| | - Simona Bartova
- Nestlé Research, EPFL Innovation Park, 1015 Lausanne, Switzerland
| | | | | | | | - Ivan Montoliu
- Nestlé Research, EPFL Innovation Park, 1015 Lausanne, Switzerland
| | - Sofia Moco
- Nestlé Research, EPFL Innovation Park, 1015 Lausanne, Switzerland
| | - Santiago Marco
- Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, 08028 Barcelona, Spain.,Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona 08028, Spain
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26
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Miros FN, Murch SJ, Shipley PR. Exploring feature selection of St John's wort grown under different light spectra using 1 H-NMR spectroscopy. PHYTOCHEMICAL ANALYSIS : PCA 2020; 31:670-680. [PMID: 32314473 DOI: 10.1002/pca.2932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 02/21/2020] [Accepted: 02/28/2020] [Indexed: 06/11/2023]
Abstract
INTRODUCTION Nuclear magnetic resonance (NMR) spectroscopy combined with multivariate statistical analysis can provide tools to help detect differences in plant chemistry when grown under varying conditions. Hypericum perforatum, or Saint John's wort, plants are a suitable model to explore methods of discrimination between early stage plants grown in different conditions. OBJECTIVES The purpose of this work was to develop a method for identifying differences in chemical profiles between young Hypericum perforatum plants grown under different lighting conditions. MATERIAL AND METHODS Cuttings were grown for 3 weeks under different light conditions. Plant extracts were prepared in MeOD-d4 and analysed by 1 H-NMR. A multivariate analysis method of the NMR data was developed in an effort to determine variations in chemical profiles. RESULTS The method identified specific metabolites as drivers of difference between the plants grown under different light conditions. STOCSY (statistical total correlation spectroscopy) and quantification of highlighted metabolites supported the findings of the multivariate analysis. Glutamine, sucrose and fructose were found to be chemical markers of light quality in this study. CONCLUSION NMR metabolomics using a medium field instrument could find differences in plant chemistry when grown in different conditions. This method could easily be extended to benchtop instruments and be used for crop monitoring and growth condition optimisation.
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Affiliation(s)
- François N Miros
- Department of Chemistry, University of British Columbia Okanagan, Kelowna, BC, Canada
| | - Susan J Murch
- Department of Chemistry, University of British Columbia Okanagan, Kelowna, BC, Canada
| | - Paul R Shipley
- Department of Chemistry, University of British Columbia Okanagan, Kelowna, BC, Canada
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27
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Lefort G, Liaubet L, Canlet C, Tardivel P, Père MC, Quesnel H, Paris A, Iannuccelli N, Vialaneix N, Servien R. ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra. Bioinformatics 2020; 35:4356-4363. [PMID: 30977816 DOI: 10.1093/bioinformatics/btz248] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 03/01/2019] [Accepted: 04/08/2019] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION In metabolomics, the detection of new biomarkers from Nuclear Magnetic Resonance (NMR) spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way. RESULTS We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketing. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the condition. AVAILABILITY AND IMPLEMENTATION ASICS is distributed as an R package, available on Bioconductor. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gaëlle Lefort
- MIAT, Université de Toulouse, INRA, Castanet Tolosan, France.,GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, France
| | - Laurence Liaubet
- GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, France
| | - Cécile Canlet
- Toxalim, Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France.,Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, Toulouse, France
| | - Patrick Tardivel
- Institute of Mathematics, University of Wroclaw, Wroclaw 50-384, Poland
| | | | | | - Alain Paris
- Unité Molécules de Communication et Adaptation des Microorganismes (MCAM), Muséum national d'Histoire naturelle, CNRS, CP54, Paris, France
| | | | | | - Rémi Servien
- INTHERES, Université de Toulouse, INRA, ENVT, Toulouse, France
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28
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Bliziotis NG, Engelke UFH, Aspers RLEG, Engel J, Deinum J, Timmers HJLM, Wevers RA, Kluijtmans LAJ. A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics. Metabolomics 2020; 16:64. [PMID: 32358672 PMCID: PMC7196944 DOI: 10.1007/s11306-020-01686-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 04/23/2020] [Indexed: 12/15/2022]
Abstract
INTRODUCTION When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr-Purcell-Meiboom-Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. OBJECTIVES Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. METHODS We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares-Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. RESULTS We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability, ~ 85% of all peaks had an adequately low coefficient of variation (< 30%) in replicate samples. CONCLUSION Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.
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Affiliation(s)
- Nikolaos G. Bliziotis
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Udo F. H. Engelke
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Ruud L. E. G. Aspers
- Institute for Molecules and Materials, Radboud University, Houtlaan 4, 6525 XZ Nijmegen, The Netherlands
| | - Jasper Engel
- Institute for Molecules and Materials, Radboud University, Houtlaan 4, 6525 XZ Nijmegen, The Netherlands
- Present Address: Biometris, Wageningen UR, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Jaap Deinum
- Department of Internal Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Henri J. L. M. Timmers
- Department of Internal Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Ron A. Wevers
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Leo A. J. Kluijtmans
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
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29
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Abstract
In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.
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Affiliation(s)
- Wimal Pathmasiri
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA.
| | - Kristine Kay
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Susan McRitchie
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Susan Sumner
- Department of Nutrition, School of Public Health, NIH Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC), Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
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30
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Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders. Prog Neuropsychopharmacol Biol Psychiatry 2019; 93:182-188. [PMID: 30904564 DOI: 10.1016/j.pnpbp.2019.03.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 02/21/2019] [Accepted: 03/20/2019] [Indexed: 01/14/2023]
Abstract
Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.
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Misra BB, Mohapatra S. Tools and resources for metabolomics research community: A 2017-2018 update. Electrophoresis 2018; 40:227-246. [PMID: 30443919 DOI: 10.1002/elps.201800428] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 11/09/2018] [Accepted: 11/09/2018] [Indexed: 01/09/2023]
Abstract
The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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