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Newton-Tanzer E, Can SN, Demmelmair H, Horak J, Holdt L, Koletzko B, Grote V. Apparent Saturation of Branched-Chain Amino Acid Catabolism After High Dietary Milk Protein Intake in Healthy Adults. J Clin Endocrinol Metab 2024:dgae599. [PMID: 39302872 DOI: 10.1210/clinem/dgae599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Indexed: 09/22/2024]
Abstract
CONTEXT Milk protein contains high concentrations of branched-chain amino acids (BCAA) that play a critical role in anabolism and are implicated in the onset of obesity and chronic disease. Characterizing BCAA catabolism in the postprandial phase could elucidate the impact of protein intake on obesity risk established in the "early protein hypothesis." OBJECTIVE To examine the acute effects of protein content of young child formulas as test meals on BCAA catabolism, observing postprandial plasma concentrations of BCAA in relation to their degradation products. METHODS The TOMI Add-On Study is a randomized, double-blind crossover study in which 27 healthy adults consumed 2 isocaloric young child formulas with alternating higher (HP) and lower (LP) protein and fat content as test meals during separate interventions, while 9 blood samples were obtained over 5 hours. BCAA, branched-chain α-keto acids (BCKA), and acylcarnitines were analyzed using a fully targeted HPLC-ESI-MS/MS approach. RESULTS Mean concentrations of BCAA, BCKA, and acylcarnitines were significantly higher after HP than LP over the 5 postprandial hours, except for the BCKA α-ketoisovalerate (KIVA). The latter metabolite showed higher postprandial concentrations after LP. With increasing mean concentrations of BCAA, concentrations of corresponding BCKA, acylcarnitines, and urea increased until a breakpoint was reached, after which concentrations of degradation products decreased (for all metabolites except valine and KIVA and Carn C4:0-iso). CONCLUSION BCAA catabolism is markedly influenced by protein content of the test meal. We present novel evidence for the apparent saturation of the BCAA degradation pathway in the acute postprandial phase up to 5 hours after consumption.
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Affiliation(s)
- Emily Newton-Tanzer
- Division of Metabolic and Nutritional Medicine, Department Paediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital, LMU Munich, and the German Center for Child and Adolescent Health, site Munich, 80337 Munich, Germany
| | - Sultan Nilay Can
- Division of Metabolic and Nutritional Medicine, Department Paediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital, LMU Munich, and the German Center for Child and Adolescent Health, site Munich, 80337 Munich, Germany
| | - Hans Demmelmair
- Division of Metabolic and Nutritional Medicine, Department Paediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital, LMU Munich, and the German Center for Child and Adolescent Health, site Munich, 80337 Munich, Germany
| | - Jeannie Horak
- Division of Metabolic and Nutritional Medicine, Department Paediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital, LMU Munich, and the German Center for Child and Adolescent Health, site Munich, 80337 Munich, Germany
| | - Lesca Holdt
- Institute of Laboratory Medicine, LMU University Hospital, LMU Munich, 80337 Munich, Germany
| | - Berthold Koletzko
- Division of Metabolic and Nutritional Medicine, Department Paediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital, LMU Munich, and the German Center for Child and Adolescent Health, site Munich, 80337 Munich, Germany
| | - Veit Grote
- Division of Metabolic and Nutritional Medicine, Department Paediatrics, Dr. von Hauner Children's Hospital, LMU University Hospital, LMU Munich, and the German Center for Child and Adolescent Health, site Munich, 80337 Munich, Germany
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2
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Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data. Metabolites 2022; 12:metabo12090812. [PMID: 36144216 PMCID: PMC9501206 DOI: 10.3390/metabo12090812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/20/2022] [Accepted: 08/25/2022] [Indexed: 11/17/2022] Open
Abstract
Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarkers.
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Density and Diversity Differences of Contemporary and Subfossil Cladocera Assemblages: A Case Study in an Oxbow Lake. WATER 2022. [DOI: 10.3390/w14142149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cladocerans are biological indicators of environmental changes. Their remains provide information on past changes in lake environments. We studied the correspondence between contemporary Cladocera assemblages and their subfossil remains from an oxbow lake. We sought to demonstrate that there were differences among the various sites of an oxbow lake with different utilization based on contemporary and subfossil Cladocera assemblages and physical–chemical variables. The oxbow lake’s two sides are used as fishing sites, where angling is the main activity. The middle site of the lake is under nature protection with high macrovegetation coverage. Contemporary and subfossil Cladocera assemblages were sampled from 21 sampling sites along the oxbow lake. Our research showed that the subfossil Cladocera assemblages had higher species richness and densities (36 taxa) than the contemporary species (29 taxa). We found one species of the Polyphemidae family only in the contemporary assemblage. Among the sites, Cladocera assemblages differed in their species composition and density. The highest densities were found in the second fishing site due to the appearance of the small-sized Bosmids. The relationship between Cladocerans and the physical–chemical variables showed that some variables, such as chlorophyll-a, biological oxygen demand, dissolved oxygen, copper, phosphide, and organic matter content, significantly affected Cladocera composition. We found that the subfossil Cladocera assemblage was significantly more diverse and abundant than the contemporary one, indicating that an integrated sampling may be sufficient to provide better results on the total species composition of the water body.
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Music of metagenomics-a review of its applications, analysis pipeline, and associated tools. Funct Integr Genomics 2021; 22:3-26. [PMID: 34657989 DOI: 10.1007/s10142-021-00810-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 09/25/2021] [Accepted: 10/03/2021] [Indexed: 10/20/2022]
Abstract
This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."
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5
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Escriba-Montagut X, Basagaña X, Vrijheid M, Gonzalez JR. Software Application Profile: exposomeShiny—a toolbox for exposome data analysis. Int J Epidemiol 2021. [PMCID: PMC8855999 DOI: 10.1093/ije/dyab220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Motivation Studying the role of the exposome in human health and its impact on different omic layers requires advanced statistical methods. Many of these methods are implemented in different R and Bioconductor packages, but their use may require strong expertise in R, in writing pipelines and in using new R classes which may not be familiar to non-advanced users. ExposomeShiny provides a bridge between researchers and most of the state-of-the-art exposome analysis methodologies, without the need of advanced programming skills. Implementation ExposomeShiny is a standalone web application implemented in R. It is available as source files and can be installed in any server or computer avoiding problems with data confidentiality. It is executed in RStudio which opens a browser window with the web application. General features The presented implementation allows the conduct of: (i) data pre-processing: normalization and missing imputation (including limit of detection); (ii) descriptive analysis; (iii) exposome principal component analysis (PCA) and hierarchical clustering; (iv) exposome-wide association studies (ExWAS) and variable selection ExWAS; (v) omic data integration by single association and multi-omic analyses; and (vi) post-exposome data analyses to gain biological insight for the exposures, genes or using the Comparative Toxicogenomics Database (CTD) and pathway analysis. Availability The exposomeShiny source code is freely available on Github at [https://github.com/isglobal-brge/exposomeShiny], Git tag v1.4. The software is also available as a Docker image [https://hub.docker.com/r/brgelab/exposome-shiny], tag v1.4. A user guide with information about the analysis methodologies as well as information on how to use exposomeShiny is freely hosted at [https://isglobal-brge.github.io/exposome_bookdown/].
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Affiliation(s)
| | - Xavier Basagaña
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Martine Vrijheid
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Department of Mathematics, Universitat Autònoma de Barcelona (UAB), Bellaterra (Barcelona), Spain
| | - Juan R Gonzalez
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Department of Mathematics, Universitat Autònoma de Barcelona (UAB), Bellaterra (Barcelona), Spain
- Corresponding author. Barcelona Biomedical Research Park (PRBB), Doctor Aiguader, 88. 08003 Barcelona, Spain. E-mail:
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6
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Bottolo L, Banterle M, Richardson S, Ala-Korpela M, Järvelin MR, Lewin A. A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery. J R Stat Soc Ser C Appl Stat 2021; 70:886-908. [PMID: 35001978 PMCID: PMC7612194 DOI: 10.1111/rssc.12490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the 31-year follow-up of the Northern Finland Birth Cohort 1966 (NFBC66). These metabolites, as with many multivariate phenotypes produced by high-throughput biomarker technology, exhibit strong correlation structures. Existing approaches for combining such data with genetic variants for multivariate QTL analysis generally ignore phenotypic correlations or make restrictive assumptions about the associations between phenotypes and genetic loci. We present a computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional data, with cell-sparse variable selection and sparse graphical structure for covariance selection. Cell sparsity allows different phenotype responses to be associated with different genetic predictors and the graphical structure is used to represent the conditional dependencies between phenotype variables. To achieve feasible computation of the large model space, we exploit a factorisation of the covariance matrix. Applying the model to the NFBC66 data with 9000 directly genotyped single nucleotide polymorphisms, we are able to simultaneously estimate genotype-phenotype associations and the residual dependence structure among the metabolites. The R package BayesSUR with full documentation is available at https://cran.r-project.org/web/packages/BayesSUR/.
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Affiliation(s)
- Leonardo Bottolo
- Department of Medical Genetics, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
- MRC Biostatistics Unit, Cambridge, UK
| | - Marco Banterle
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Sylvia Richardson
- The Alan Turing Institute, London, UK
- MRC Biostatistics Unit, Cambridge, UK
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Department of Life Sciences, Brunel University London, Uxbridge, UK
| | - Alex Lewin
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
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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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8
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Abstract
Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.
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9
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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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10
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Rodriguez-Martinez A, Ayala R, Posma JM, Harvey N, Jiménez B, Sonomura K, Sato TA, Matsuda F, Zalloua P, Gauguier D, Nicholson JK, Dumas ME. pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra. Bioinformatics 2020; 35:1916-1922. [PMID: 30351417 PMCID: PMC6546129 DOI: 10.1093/bioinformatics/bty837] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 08/24/2018] [Accepted: 10/22/2018] [Indexed: 01/21/2023] Open
Abstract
Motivation Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra. Results The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building Availability and implementation The algorithm is implemented using the MWASTools R/Bioconductor package. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrea Rodriguez-Martinez
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.,Department of Epidemiology and Biostatistics School of Public Health, Imperial College London, London, UK
| | - Rafael Ayala
- Section of Structural Biology, Department of Medicine, Shimadzu Corporation, Kyoto, Japan
| | - Joram M Posma
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.,Department of Epidemiology and Biostatistics School of Public Health, Imperial College London, London, UK
| | - Nikita Harvey
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Beatriz Jiménez
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Kazuhiro Sonomura
- Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, Japan.,Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Taka-Aki Sato
- Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, Japan.,Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Pierre Zalloua
- School of Medicine, Lebanese American University, Beirut, Lebanon
| | - Dominique Gauguier
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Cordeliers Research Centre, INSERM UMR_S, Paris, France
| | - Jeremy K Nicholson
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Marc-Emmanuel Dumas
- Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK
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11
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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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12
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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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13
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Brennan L, Hu FB. Metabolomics-Based Dietary Biomarkers in Nutritional Epidemiology-Current Status and Future Opportunities. Mol Nutr Food Res 2019; 63:e1701064. [PMID: 29688616 DOI: 10.1002/mnfr.201701064] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 03/27/2018] [Indexed: 01/04/2023]
Abstract
The application of metabolomics in nutrition epidemiology holds great promise and there is a high expectation that it will play a leading role in deciphering the interactions between diet and health. However, while significant progress has been made in the identification of putative biomarkers, more work is needed to address the use of the biomarkers in dietary assessment. The aim of this review is to critically evaluate progress in these areas and to identify challenges that need to be addressed going forward. The notable applications of dietary biomarkers in nutritional epidemiology include 1) determination of food intake based on biomarkers levels and calibration equations from feeding studies, 2) classification of individuals into dietary patterns based on the urinary metabolic profile, and 3) application of metabolome wide-association studies. Further work is needed to address some specific challenges to enable biomarkers to reach their full potential.
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Affiliation(s)
- Lorraine Brennan
- UCD School of Agriculture and Food Science, University College Dublin (UCD), Belfield, Dublin 4, Ireland
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.,Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA.,Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA
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14
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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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15
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Rodriguez-Martinez A, Posma JM, Ayala R, Harvey N, Jimenez B, Neves AL, Lindon JC, Sonomura K, Sato TA, Matsuda F, Zalloua P, Gauguier D, Nicholson JK, Dumas ME. J-Resolved 1H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis. Anal Chem 2017; 89:11405-11412. [DOI: 10.1021/acs.analchem.7b02374] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Andrea Rodriguez-Martinez
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Joram M. Posma
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Rafael Ayala
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Nikita Harvey
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Beatriz Jimenez
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Ana L. Neves
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - John C. Lindon
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Kazuhiro Sonomura
- Life
Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto 604-8511, Japan
- Center
for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Taka-Aki Sato
- Life
Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto 604-8511, Japan
- Center
for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Fumihiko Matsuda
- Center
for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan
| | - Pierre Zalloua
- School
of Medicine, Lebanese American University, Beirut 1102 2801, Lebanon
| | - Dominique Gauguier
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
- Cordeliers Research
Centre, INSERM UMR_S 1138, 75006 Paris, France
| | - Jeremy K. Nicholson
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
| | - Marc-Emmanuel Dumas
- Computational
and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, U.K
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