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Wang M, Vijayaraghavan A, Beck T, Posma JM. Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition. J Proteome Res 2024. [PMID: 38733346 DOI: 10.1021/acs.jproteome.3c00367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
Enzymes are indispensable in many biological processes, and with biomedical literature growing exponentially, effective literature review becomes increasingly challenging. Natural language processing methods offer solutions to streamline this process. This study aims to develop an annotated enzyme corpus for training and evaluating enzyme named entity recognition (NER) models. A novel pipeline, combining dictionary matching and rule-based keyword searching, automatically annotated enzyme entities in >4800 full-text publications. Four deep learning NER models were created with different vocabularies (BioBERT/SciBERT) and architectures (BiLSTM/transformer) and evaluated on 526 manually annotated full-text publications. The annotation pipeline achieved an F1-score of 0.86 (precision = 1.00, recall = 0.76), surpassed by fine-tuned transformers for F1-score (BioBERT: 0.89, SciBERT: 0.88) and recall (0.86) with BiLSTM models having higher precision (0.94) than transformers (0.92). The annotation pipeline runs in seconds on standard laptops with almost perfect precision, but was outperformed by fine-tuned transformers in terms of F1-score and recall, demonstrating generalizability beyond the training data. In comparison, SciBERT-based models exhibited higher precision, and BioBERT-based models exhibited higher recall, highlighting the importance of vocabulary and architecture. These models, representing the first enzyme NER algorithms, enable more effective enzyme text mining and information extraction. Codes for automated annotation and model generation are available from https://github.com/omicsNLP/enzymeNER and https://zenodo.org/doi/10.5281/zenodo.10581586.
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Affiliation(s)
- Meiqi Wang
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, U.K
| | - Avish Vijayaraghavan
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, U.K
- UKRI Centre for Doctoral Training in AI for Healthcare, Department of Computing, Imperial College London, London SW7 2AZ, U.K
| | - Tim Beck
- School of Medicine, University of Nottingham, Biodiscovery Institute, Nottingham NG7 2RD, U.K
- Health Data Research (HDR) U.K., London NW1 2BE, U.K
| | - Joram M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, U.K
- Health Data Research (HDR) U.K., London NW1 2BE, U.K
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Eriksen R, White MC, Dawed AY, Perez IG, Posma JM, Haid M, Sharma S, Prehn C, Thomas LE, Koivula RW, Bizzotto R, Mari A, Giordano GN, Pavo I, Schwenk JM, De Masi F, Tsirigos KD, Brunak S, Viñuela A, Mahajan A, McDonald TJ, Kokkola T, Rutters F, Beulens J, Muilwijk M, Blom M, Elders P, Hansen TH, Fernandez-Tajes J, Jones A, Jennison C, Walker M, McCarthy MI, Pedersen O, Ruetten H, Forgie I, Holst JJ, Thomsen HS, Ridderstråle M, Bell JD, Adamski J, Franks PW, Hansen T, Holmes E, Frost G, Pearson ER. The association of cardiometabolic, diet and lifestyle parameters with plasma glucagon-like peptide-1: An IMI DIRECT study. J Clin Endocrinol Metab 2024:dgae119. [PMID: 38686701 DOI: 10.1210/clinem/dgae119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/20/2023] [Accepted: 02/27/2024] [Indexed: 05/02/2024]
Abstract
CONTEXT The role of glucagon-like peptide-1(GLP-1) in Type 2 diabetes (T2D) and obesity is not fully understood. OBJECTIVE We investigate the association of cardiometabolic, diet and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. METHOD We analysed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1(n=2127) individuals at risk of diabetes; cohort 2 (n=789) individuals with new-onset of T2D. RESULTS Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin resistant phenotype and observe a strong independent relationship with male sex, increased adiposity and liver fat particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycaemia, higher adiposity, liver fat, male sex and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit and vegetables inpeople with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. CONCLUSION These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D.
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Affiliation(s)
- Rebeca Eriksen
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, UK
| | - Margaret C White
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Adem Y Dawed
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Isabel Garcia Perez
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, UK
| | - Joram M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, UK
- Health Data Research UK, London, UK
| | - Mark Haid
- Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany
| | - Sapna Sharma
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
| | - Cornelia Prehn
- Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany
| | - Louise E Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Robert W Koivula
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Radcliffe Department of Medicine, Oxford, UK
| | - Roberto Bizzotto
- Institute of Neuroscience - National Research Council, Padova, Italy
| | - Andrea Mari
- Institute of Neuroscience - National Research Council, Padova, Italy
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Federico De Masi
- Department of Health Technology, Technical University of Denmark, Kgs Lyngby and The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Konstantinos D Tsirigos
- Department of Health Technology, Technical University of Denmark, Kgs Lyngby and The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Department of Health Technology, Technical University of Denmark, Kgs Lyngby and The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Ana Viñuela
- Biosciences Institute, Newcastle University. Newcastle upon Tyne. United Kingdom
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Timothy J McDonald
- Medical School, Exeter, UK NIHR Exeter Clinical Research Facility, University of Exeter
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Femke Rutters
- Department of Epidemiology and data Science, Amsterdam Public Health Institute, Amsterdam UMC, location VUMC, Amsterdam, The Netherlands
| | - Joline Beulens
- Department of Epidemiology and data Science, Amsterdam Public Health Institute, Amsterdam UMC, location VUMC, Amsterdam, The Netherlands
| | - Mirthe Muilwijk
- Department of Epidemiology and data Science, Amsterdam Public Health Institute, Amsterdam UMC, location VUMC, Amsterdam, The Netherlands
| | - Marieke Blom
- Department of Epidemiology and data Science, Amsterdam Public Health Institute, Amsterdam UMC, location VUMC, Amsterdam, The Netherlands
| | - Petra Elders
- Department of Epidemiology and data Science, Amsterdam Public Health Institute, Amsterdam UMC, location VUMC, Amsterdam, The Netherlands
| | - Tue H Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | | | - Angus Jones
- Medical School, Exeter, UK NIHR Exeter Clinical Research Facility, University of Exeter
| | - Chris Jennison
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | - Mark Walker
- Institute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, UK
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Radcliffe Department of Medicine, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Hartmut Ruetten
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - Ian Forgie
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Jens J Holst
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik S Thomsen
- Faculty of Medical and Health Sciences, University of Copenhagen, Denmark
| | | | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, 85350 Freising-Weihenstephan, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Elaine Holmes
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, UK
| | - Gary Frost
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, UK
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
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Kasapi M, Xu K, Ebbels TMD, O'Regan DP, Ware JS, Posma JM. LAVASET: Latent Variable Stochastic Ensemble of Trees. An ensemble method for correlated datasets with spatial, spectral, and temporal dependencies. Bioinformatics 2024; 40:btae101. [PMID: 38383048 DOI: 10.1093/bioinformatics/btae101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/30/2024] [Accepted: 02/20/2024] [Indexed: 02/23/2024] Open
Abstract
MOTIVATION Random forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction. However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting. Addressing correlation relationships in high-dimensional datasets is imperative for reducing the number of variables that are assigned high importance, hence making the dimension reduction most efficient. Here, we propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each feature and aims to incorporate the correlation factor in the splitting step. RESULTS Without compromising on performance in the majority of examples, LAVASET outperforms RF by accurately determining feature importance across all correlated variables and ensuring proper distribution of importance values. LAVASET yields mostly non-inferior prediction accuracies to traditional RFs when tested in simulated and real 1D datasets, as well as more complex and high-dimensional 3D datatypes. Unlike traditional RFs, LAVASET is unaffected by single 'important' noisy features (false positives), as it considers the local neighbourhood. LAVASET, therefore, highlights neighbourhoods of features, reflecting real signals that collectively impact the model's predictive ability. AVAILABILITY AND IMPLEMENTATION LAVASET is freely available as a standalone package from https://github.com/melkasapi/LAVASET.
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Affiliation(s)
- Melpomeni Kasapi
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London W12 0NN, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, United Kingdom
| | - Kexin Xu
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
| | - Timothy M D Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
| | - Declan P O'Regan
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London W12 0NN, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, United Kingdom
| | - James S Ware
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London W12 0NN, United Kingdom
- MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, United Kingdom
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London SW3 6NP, United Kingdom
- Program in Medical & Population Genetics, Broad Institute of MIT & Harvard, Cambridge, MA 02142, United States
| | - Joram M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
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Boubnovski Martell M, Linton-Reid K, Hindocha S, Chen M, Moreno P, Álvarez-Benito M, Salvatierra Á, Lee R, Posma JM, Calzado MA, Aboagye EO. Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis. NPJ Precis Oncol 2024; 8:28. [PMID: 38310164 PMCID: PMC10838282 DOI: 10.1038/s41698-024-00502-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/04/2024] [Indexed: 02/05/2024] Open
Abstract
The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients' prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.
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Affiliation(s)
| | | | - Sumeet Hindocha
- Early Diagnosis and Detection Centre, National Institute for Health and Care Research Biomedical Research Centre at the Royal Marsden and Institute of Cancer Research, London, SW3 6JJ, UK
| | - Mitchell Chen
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK
| | - Paula Moreno
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Departamento de Cirugía Toráxica y Trasplante de Pulmón, Hospital Universitario Reina Sofía, Córdoba, 14014, Spain
| | - Marina Álvarez-Benito
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Unidad de Radiodiagnóstico y Cáncer de Mama, Hospital Universitario Reina Sofía, Córdoba, 14004, Spain
| | - Ángel Salvatierra
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Unidad de Radiodiagnóstico y Cáncer de Mama, Hospital Universitario Reina Sofía, Córdoba, 14004, Spain
| | - Richard Lee
- Early Diagnosis and Detection Centre, National Institute for Health and Care Research Biomedical Research Centre at the Royal Marsden and Institute of Cancer Research, London, SW3 6JJ, UK
- National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse Street, London, SW3 6LY, UK
| | - Joram M Posma
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK
| | - Marco A Calzado
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain.
- Departamento de Biología Celular, Fisiología e Inmunología, Universidad de Córdoba, Córdoba, 14014, Spain.
| | - Eric O Aboagye
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK.
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Rundle M, Fiamoncini J, Thomas EL, Wopereis S, Afman LA, Brennan L, Drevon CA, Gundersen TE, Daniel H, Perez IG, Posma JM, Ivanova DG, Bell JD, van Ommen B, Frost G. Diet-induced Weight Loss and Phenotypic Flexibility Among Healthy Overweight Adults: A Randomized Trial. Am J Clin Nutr 2023; 118:591-604. [PMID: 37661105 PMCID: PMC10517213 DOI: 10.1016/j.ajcnut.2023.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 06/28/2023] [Accepted: 07/03/2023] [Indexed: 09/05/2023] Open
Abstract
BACKGROUND The capacity of an individual to respond to changes in food intake so that postprandial metabolic perturbations are resolved, and metabolism returns to its pre-prandial state, is called phenotypic flexibility. This ability may be a more important indicator of current health status than metabolic markers in a fasting state. AIM In this parallel randomized controlled trial study, an energy-restricted healthy diet and 2 dietary challenges were used to assess the effect of weight loss on phenotypic flexibility. METHODS Seventy-two volunteers with overweight and obesity underwent a 12-wk dietary intervention. The participants were randomized to a weight loss group (WLG) with 20% less energy intake or a weight-maintenance group (WMG). At weeks 1 and 12, participants were assessed for body composition by MRI. Concurrently, markers of metabolism and insulin sensitivity were obtained from the analysis of plasma metabolome during 2 different dietary challenges-an oral glucose tolerance test (OGTT) and a mixed-meal tolerance test. RESULTS Intended weight loss was achieved in the WLG (-5.6 kg, P < 0.0001) and induced a significant reduction in total and regional adipose tissue as well as ectopic fat in the liver. Amino acid-based markers of insulin action and resistance such as leucine and glutamate were reduced in the postprandial phase of the OGTT in the WLG by 11.5% and 28%, respectively, after body weight reduction. Weight loss correlated with the magnitude of changes in metabolic responses to dietary challenges. Large interindividual variation in metabolic responses to weight loss was observed. CONCLUSION Application of dietary challenges increased sensitivity to detect metabolic response to weight loss intervention. Large interindividual variation was observed across a wide range of measurements allowing the identification of distinct responses to the weight loss intervention and mechanistic insight into the metabolic response to weight loss.
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Affiliation(s)
- Milena Rundle
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Jarlei Fiamoncini
- Food Research Center, Department of Food Science and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Suzan Wopereis
- Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research, Hague, The Netherlands
| | - Lydia A Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Belfield, Dublin, Ireland
| | - Christian A Drevon
- Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway; Vitas Ltd, Oslo Science Park, Oslo, Norway
| | | | - Hannelore Daniel
- Hannelore Daniel, Molecular Nutrition Unit, Technische Universität München, München, Germany
| | - Isabel Garcia Perez
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Joram M Posma
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Diana G Ivanova
- Department of Biochemistry, Molecular Medicine and Nutrigenomics, Faculty of Pharmacy, Medical University, Varna, Bulgaria
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Ben van Ommen
- Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research, Hague, The Netherlands
| | - Gary Frost
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom.
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6
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Alexander JL, Posma JM, Scott A, Poynter L, Mason SE, Doria ML, Herendi L, Roberts L, McDonald JAK, Cameron S, Hughes DJ, Liska V, Susova S, Soucek P, der Sluis VHV, Gomez-Romero M, Lewis MR, Hoyles L, Woolston A, Cunningham D, Darzi A, Gerlinger M, Goldin R, Takats Z, Marchesi JR, Teare J, Kinross J. Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer. Microbiome 2023; 11:100. [PMID: 37158960 PMCID: PMC10165813 DOI: 10.1186/s40168-023-01518-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 03/15/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND AIMS The gut microbiota is implicated in the pathogenesis of colorectal cancer (CRC). We aimed to map the CRC mucosal microbiota and metabolome and define the influence of the tumoral microbiota on oncological outcomes. METHODS A multicentre, prospective observational study was conducted of CRC patients undergoing primary surgical resection in the UK (n = 74) and Czech Republic (n = 61). Analysis was performed using metataxonomics, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), targeted bacterial qPCR and tumour exome sequencing. Hierarchical clustering accounting for clinical and oncological covariates was performed to identify clusters of bacteria and metabolites linked to CRC. Cox proportional hazards regression was used to ascertain clusters associated with disease-free survival over median follow-up of 50 months. RESULTS Thirteen mucosal microbiota clusters were identified, of which five were significantly different between tumour and paired normal mucosa. Cluster 7, containing the pathobionts Fusobacterium nucleatum and Granulicatella adiacens, was strongly associated with CRC (PFDR = 0.0002). Additionally, tumoral dominance of cluster 7 independently predicted favourable disease-free survival (adjusted p = 0.031). Cluster 1, containing Faecalibacterium prausnitzii and Ruminococcus gnavus, was negatively associated with cancer (PFDR = 0.0009), and abundance was independently predictive of worse disease-free survival (adjusted p = 0.0009). UPLC-MS analysis revealed two major metabolic (Met) clusters. Met 1, composed of medium chain (MCFA), long-chain (LCFA) and very long-chain (VLCFA) fatty acid species, ceramides and lysophospholipids, was negatively associated with CRC (PFDR = 2.61 × 10-11); Met 2, composed of phosphatidylcholine species, nucleosides and amino acids, was strongly associated with CRC (PFDR = 1.30 × 10-12), but metabolite clusters were not associated with disease-free survival (p = 0.358). An association was identified between Met 1 and DNA mismatch-repair deficiency (p = 0.005). FBXW7 mutations were only found in cancers predominant in microbiota cluster 7. CONCLUSIONS Networks of pathobionts in the tumour mucosal niche are associated with tumour mutation and metabolic subtypes and predict favourable outcome following CRC resection. Video Abstract.
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Affiliation(s)
- James L Alexander
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK
- Department of Gastroenterology, Imperial College Healthcare NHS Trust, London, UK
| | - Joram M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Alasdair Scott
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Liam Poynter
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Sam E Mason
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - M Luisa Doria
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Lili Herendi
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Lauren Roberts
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK
| | - Julie A K McDonald
- Department of Life Sciences, MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
| | - Simon Cameron
- Institute of Global Food Security, School of Biosciences, Queen's University Belfast, Belfast, UK
| | - David J Hughes
- Cancer Biology and Therapeutics Group, School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Vaclav Liska
- Department of Surgery, Faculty Hospital and Faculty of Medicine in Pilsen, Charles University in Prague, Pilsen, Czech Republic
| | - Simona Susova
- Faculty of Medicine in Pilsen, Biomedical Centre, Charles University in Prague, Pilsen, Czech Republic
| | - Pavel Soucek
- Faculty of Medicine in Pilsen, Biomedical Centre, Charles University in Prague, Pilsen, Czech Republic
| | - Verena Horneffer-van der Sluis
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Maria Gomez-Romero
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Matthew R Lewis
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Lesley Hoyles
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK
- Department of Biosciences, Nottingham Trent University, Nottingham, NG11 8NS, UK
| | - Andrew Woolston
- Translational Oncogenomics Laboratory, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK
| | - David Cunningham
- GI Cancer Unit, Department of Medical Oncology, Royal Marsden NHS Foundation Trust, London, UK
| | - Ara Darzi
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Marco Gerlinger
- Translational Oncogenomics Laboratory, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK
- GI Cancer Unit, Department of Medical Oncology, Royal Marsden NHS Foundation Trust, London, UK
| | - Robert Goldin
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK
| | - Zoltan Takats
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, London, UK
| | - Julian R Marchesi
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, 10th Floor, QEQM Building, St. Mary's Hospital, Praed Street, London, W2 1NY, UK.
| | - Julian Teare
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - James Kinross
- Department of Surgery & Cancer, Imperial College London, London, UK
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7
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Boubnovski MM, Chen M, Linton-Reid K, Posma JM, Copley SJ, Aboagye EO. Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs. Clin Radiol 2022; 77:e620-e627. [PMID: 35636974 DOI: 10.1016/j.crad.2022.04.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 04/21/2022] [Indexed: 02/08/2023]
Abstract
AIM To develop a multi-task learning (MTL) V-Net for pulmonary lobar segmentation on computed tomography (CT) and application to diseased lungs. MATERIALS AND METHODS The described methodology utilises tracheobronchial tree information to enhance segmentation accuracy through the algorithm's spatial familiarity to define lobar extent more accurately. The method undertakes parallel segmentation of lobes and auxiliary tissues simultaneously by employing MTL in conjunction with V-Net-attention, a popular convolutional neural network in the imaging realm. Its performance was validated by an external dataset of patients with four distinct lung conditions: severe lung cancer, COVID-19 pneumonitis, collapsed lungs, and chronic obstructive pulmonary disease (COPD), even though the training data included none of these cases. RESULTS The following Dice scores were achieved on a per-segment basis: normal lungs 0.97, COPD 0.94, lung cancer 0.94, COVID-19 pneumonitis 0.94, and collapsed lung 0.92, all at p<0.05. CONCLUSION Despite severe abnormalities, the model provided good performance at segmenting lobes, demonstrating the benefit of tissue learning. The proposed model is poised for adoption in the clinical setting as a robust tool for radiologists and researchers to define the lobar distribution of lung diseases and aid in disease treatment planning.
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Affiliation(s)
- M M Boubnovski
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK
| | - M Chen
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK; Department of Radiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London W12 0HS, UK
| | - K Linton-Reid
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK
| | - J M Posma
- Department of Metabolism, Digestion and Reproduction, South Kensington, London SW7 2AZ, UK
| | - S J Copley
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK; Department of Radiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London W12 0HS, UK
| | - E O Aboagye
- Comprehensive Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London W12 0NN, UK.
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8
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Chan Q, Wren GM, Lau CHE, Ebbels TMD, Gibson R, Loo RL, Aljuraiban GS, Posma JM, Dyer AR, Steffen LM, Rodriguez BL, Appel LJ, Daviglus ML, Elliott P, Stamler J, Holmes E, Van Horn L. Blood pressure interactions with the DASH dietary pattern, sodium, and potassium: The International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP). Am J Clin Nutr 2022; 116:216-229. [PMID: 35285859 PMCID: PMC9257466 DOI: 10.1093/ajcn/nqac067] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/09/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet enhances potassium intake and reduces sodium intake and blood pressure (BP), but the underlying metabolic pathways are unclear. OBJECTIVES Among free-living populations, we delineated metabolic signatures associated with the DASH diet adherence, 24-hour urinary sodium and potassium excretions, and the potential metabolic pathways involved. METHODS We used 24-hour urinary metabolic profiling by proton nuclear magnetic resonance spectroscopy to characterize the metabolic signatures associated with the DASH dietary pattern score (DASH score) and 24-hour excretion of sodium and potassium among participants in the United States (n = 2164) and United Kingdom (n = 496) enrolled in the International Study of Macro- and Micronutrients and Blood Pressure (INTERMAP). Multiple linear regression and cross-tabulation analyses were used to investigate the DASH-BP relation and its modulation by sodium and potassium. Potential pathways associated with DASH adherence, sodium and potassium excretion, and BP were identified using mediation analyses and metabolic reaction networks. RESULTS Adherence to the DASH diet was associated with urinary potassium excretion (correlation coefficient, r = 0.42; P < 0.0001). In multivariable regression analyses, a 5-point higher DASH score (range, 7 to 35) was associated with a lower systolic BP by 1.35 mmHg (95% CI, -1.95 to -0.80 mmHg; P = 1.2 × 10-5); control of the model for potassium but not sodium attenuated the DASH-BP relation. Two common metabolites (hippurate and citrate) mediated the potassium-BP and DASH-BP relationships, while 5 metabolites (succinate, alanine, S-methyl cysteine sulfoxide, 4-hydroxyhippurate, and phenylacetylglutamine) were found to be specific to the DASH-BP relation. CONCLUSIONS Greater adherence to the DASH diet is associated with lower BP and higher potassium intake across levels of sodium intake. The DASH diet recommends greater intake of fruits, vegetables, and other potassium-rich foods that may replace sodium-rich processed foods and thereby influence BP through overlapping metabolic pathways. Possible DASH-specific pathways are speculated but confirmation requires further study. INTERMAP is registered as NCT00005271 at www.clinicaltrials.gov.
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Affiliation(s)
| | - Gina M Wren
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Chung-Ho E Lau
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom,Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Timothy M D Ebbels
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Rachel Gibson
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom,Department of Nutritional Sciences, King's College London, London, United Kingdom
| | - Ruey Leng Loo
- Australian National Phenome Centre and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
| | - Ghadeer S Aljuraiban
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Joram M Posma
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Alan R Dyer
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Beatriz L Rodriguez
- Department of Geriatric Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Lawrence J Appel
- Welch Center for Prevention, Epidemiology and Clinical Research; Johns Hopkins University, Baltimore, MD, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Elaine Holmes
- Section of Nutrition, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom,Australian National Phenome Centre and Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
| | - Linda Van Horn
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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9
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Beck T, Shorter T, Hu Y, Li Z, Sun S, Popovici CM, McQuibban NAR, Makraduli F, Yeung CS, Rowlands T, Posma JM. Auto-CORPus: A Natural Language Processing Tool for Standardizing and Reusing Biomedical Literature. Front Digit Health 2022; 4:788124. [PMID: 35243479 PMCID: PMC8885717 DOI: 10.3389/fdgth.2022.788124] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/21/2022] [Indexed: 11/18/2022] Open
Abstract
To analyse large corpora using machine learning and other Natural Language Processing (NLP) algorithms, the corpora need to be standardized. The BioC format is a community-driven simple data structure for sharing text and annotations, however there is limited access to biomedical literature in BioC format and a lack of bioinformatics tools to convert online publication HTML formats to BioC. We present Auto-CORPus (Automated pipeline for Consistent Outputs from Research Publications), a novel NLP tool for the standardization and conversion of publication HTML and table image files to three convenient machine-interpretable outputs to support biomedical text analytics. Firstly, Auto-CORPus can be configured to convert HTML from various publication sources to BioC. To standardize the description of heterogenous publication sections, the Information Artifact Ontology is used to annotate each section within the BioC output. Secondly, Auto-CORPus transforms publication tables to a JSON format to store, exchange and annotate table data between text analytics systems. The BioC specification does not include a data structure for representing publication table data, so we present a JSON format for sharing table content and metadata. Inline tables within full-text HTML files and linked tables within separate HTML files are processed and converted to machine-interpretable table JSON format. Finally, Auto-CORPus extracts abbreviations declared within publication text and provides an abbreviations JSON output that relates an abbreviation with the full definition. This abbreviation collection supports text mining tasks such as named entity recognition by including abbreviations unique to individual publications that are not contained within standard bio-ontologies and dictionaries. The Auto-CORPus package is freely available with detailed instructions from GitHub at: https://github.com/omicsNLP/Auto-CORPus.
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Affiliation(s)
- Tim Beck
- Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
- Health Data Research UK (HDR UK), London, United Kingdom
- *Correspondence: Tim Beck
| | - Tom Shorter
- Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Yan Hu
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Zhuoyu Li
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Shujian Sun
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Casiana M. Popovici
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Nicholas A. R. McQuibban
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- Centre for Integrative Systems Biology and Bioinformatics (CISBIO), Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Filip Makraduli
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Cheng S. Yeung
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Thomas Rowlands
- Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Joram M. Posma
- Health Data Research UK (HDR UK), London, United Kingdom
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- Joram M. Posma
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10
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Mujagic Z, Kasapi M, Jonkers DMAE, Garcia-Perez I, Vork L, Weerts ZZR, Serrano-Contreras JI, Zhernakova A, Kurilshikov A, Scotcher J, Holmes E, Wijmenga C, Keszthelyi D, Nicholson JK, Posma JM, Masclee AAM. Integrated fecal microbiome-metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome. Gut Microbes 2022; 14:2063016. [PMID: 35446234 PMCID: PMC9037519 DOI: 10.1080/19490976.2022.2063016] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS), several modalities of biological and clinical data must be combined. We aimed to identify profiles of fecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Fecal metabolites were measured using proton nuclear magnetic resonance (1H-NMR) spectroscopy and gut microbiome using shotgun metagenomic sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HCs) with extensive clinical, biological and phenotype information. Data were analyzed using support vector classification and regression and kernel t-SNE. Microbiome and metabolome profiles could distinguish IBS and HC with an area-under-the-receiver-operator-curve of 77.3% and 79.5%, respectively, but this could be improved by combining microbiota and metabolites to 83.6%. No significant differences in predictive ability of the microbiome-metabolome data were observed between the three classical, stool pattern-based, IBS subtypes. However, unsupervised clustering showed distinct subsets of IBS patients based on fecal microbiome-metabolome data. These clusters could be related plasma levels of serotonin and its metabolite 5-hydroxyindoleacetate, effects of psychological stress on gastrointestinal (GI) symptoms, onset of IBS after stressful events, medical history of previous abdominal surgery, dietary caloric intake and IBS symptom duration. Furthermore, pathways in metabolic reaction networks were integrated with microbiota data, that reflect the host-microbiome interactions in IBS. The identified microbiome-metabolome signatures for IBS, associated with altered serotonin metabolism and unfavorable stress response related to GI symptoms, support the microbiota-gut-brain link in the pathogenesis of IBS.
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Affiliation(s)
- Zlatan Mujagic
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands,Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK,CONTACT Zlatan Mujagic Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Melpomeni Kasapi
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK
| | - Daisy MAE Jonkers
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Isabel Garcia-Perez
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, London, UK
| | - Lisa Vork
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Zsa Zsa R.M. Weerts
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Jose Ivan Serrano-Contreras
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK
| | - Alexandra Zhernakova
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Alexander Kurilshikov
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jamie Scotcher
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK
| | - Elaine Holmes
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, London, UK,The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, Perth, Australia
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Daniel Keszthelyi
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Jeremy K Nicholson
- The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, Perth, Australia
| | - Joram M Posma
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, London, UK
| | - Ad AM Masclee
- Division Gastroenterology-Hepatology, Maastricht University Medical Center+, Maastricht, The Netherlands,Nutrim School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
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11
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Abbott KA, Posma JM, Garcia-Perez I, Udeh-Momoh C, Ahmadi-Abhari S, Middleton L, Frost G. Evidence-Based Tools for Dietary Assessments in Nutrition Epidemiology Studies for Dementia Prevention. J Prev Alzheimers Dis 2022; 9:49-53. [PMID: 35098973 DOI: 10.14283/jpad.2022.6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Increasing evidence proposes diet as a notable modifiable factor and viable target for the reduction of Alzheimer's Disease risk and age-related cognitive decline. However, assessment of dietary exposures is challenged by dietary capture methods that are prone to misreporting and measurement errors. The utility of -omics technologies for the evaluation of dietary exposures has the potential to improve reliability and offer new insights to pre-disease indicators and preventive targets in cognitive aging and dementia. In this review, we present a focused overview of metabolomics as a validation tool and framework for investigating the immediate or cumulative effects of diet on cognitive health.
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Affiliation(s)
- K A Abbott
- Prof Gary Frost, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London Hammersmith Campus, Commonwealth Building, Du Cane Road, London W12 ONN, United Kingdom, Tel: +44 (0)20 7594 0959. Fax: +44 (0208383 8320, Email :
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12
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Wu Y, Posma JM, Holmes E, Frost G, Chambers ES, Garcia-Perez I. Odd Chain Fatty Acids Are Not Robust Biomarkers for Dietary Intake of Fiber. Mol Nutr Food Res 2021; 65:e2100316. [PMID: 34605164 DOI: 10.1002/mnfr.202100316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/06/2021] [Indexed: 11/08/2022]
Abstract
SCOPE Prior investigation has suggested a positive association between increased colonic propionate production and circulating odd-chain fatty acids (OCFAs; pentadecanoic acid [C15:0], heptadecanoic acid [C17:0]). As the major source of propionate in humans is the microbial fermentation of dietary fiber, OCFAs have been proposed as candidate biomarkers of dietary fiber. The objective of this study is to critically assess the plausibility, robustness, reliability, dose-response, time-response aspects of OCFAs as potential biomarkers of fermentable fibers in two independent studies using a validated analytical method. METHODS AND RESULTS OCFAs are first assessed in a fiber supplementation study, where 21 participants received 10 g dietary fiber supplementation for 7 days. OCFAs are then assessed in a highly controlled inpatient setting, which 19 participants consumed a high fiber (45.1 g per day) and a low fiber diet (13.6 g per day) for 4 days. Collectively in both studies, dietary intakes of fiber as fiber supplementations or having consumed a high fiber diet do not increase circulating levels of OCFAs. The dose and temporal relations are not observed. CONCLUSION Current study has generated new insight on the utility of OCFAs as fiber biomarkers and highlighted the importance of critical assessment of candidate biomarkers before application.
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Affiliation(s)
- Yiwei Wu
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Joram M Posma
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.,Health Data Research UK-London, London, UK
| | - Elaine Holmes
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Gary Frost
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Edward S Chambers
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Isabel Garcia-Perez
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
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13
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Posma JM, Garcia-Perez I, Frost G, Aljuraiban GS, Chan Q, Van Horn L, Daviglus M, Stamler J, Holmes E, Elliott P, Nicholson JK. Author Correction: Nutriome-metabolome relationships provide insights into dietary intake and metabolism. Nat Food 2021; 2:541-542. [PMID: 37117689 DOI: 10.1038/s43016-021-00309-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Affiliation(s)
- Joram M Posma
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Health Data Research UK London, London, UK
| | - Isabel Garcia-Perez
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Gary Frost
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Ghadeer S Aljuraiban
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Queenie Chan
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Linda Van Horn
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Elaine Holmes
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
- UK Dementia Research Institute, Faculty of Medicine, Imperial College London, London, UK.
- Division of Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia.
- The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, Perth, Western Australia, Australia.
| | - Paul Elliott
- Health Data Research UK London, London, UK.
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK.
- MRC Centre for Environment and Health, School of Public Health, Faculty of Medicine, Imperial College London, London, UK.
- UK Dementia Research Institute, Faculty of Medicine, Imperial College London, London, UK.
- National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, London, UK.
- British Heart Foundation Centre of Research Excellence at Imperial, Imperial College London, London, UK.
| | - Jeremy K Nicholson
- Division of Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia.
- The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, Perth, Western Australia, Australia.
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14
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Mayneris-Perxachs J, Amaral W, Lubach GR, Lyte M, Phillips GJ, Posma JM, Coe CL, Swann JR. Gut Microbial and Metabolic Profiling Reveal the Lingering Effects of Infantile Iron Deficiency Unless Treated with Iron. Mol Nutr Food Res 2021; 65:e2001018. [PMID: 33599094 DOI: 10.1002/mnfr.202001018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 01/29/2021] [Indexed: 12/11/2022]
Abstract
SCOPE Iron deficiency (ID) compromises the health of infants worldwide. Although readily treated with iron, concerns remain about the persistence of some effects. Metabolic and gut microbial consequences of infantile ID were investigated in juvenile monkeys after natural recovery (pID) from iron deficiency or post-treatment with iron dextran and B vitamins (pID+Fe). METHODS AND RESULTS Metabolomic profiling of urine and plasma is conducted with 1 H nuclear magnetic resonance (NMR) spectroscopy. Gut microbiota are characterized from rectal swabs by amplicon sequencing of the 16S rRNA gene. Urinary metabolic profiles of pID monkeys significantly differed from pID+Fe and continuously iron-sufficient controls (IS) with higher maltose and lower amounts of microbial-derived metabolites. Persistent differences in energy metabolism are apparent from the plasma metabolic phenotypes with greater reliance on anaerobic glycolysis in pID monkeys. Microbial profiling indicated higher abundances of Methanobrevibacter, Lachnobacterium, and Ruminococcus in pID monkeys and any history of ID resulted in a lower Prevotella abundance compared to the IS controls. CONCLUSIONS Lingering metabolic and microbial effects are found after natural recovery from ID. These long-term biochemical derangements are not present in the pID+Fe animals emphasizing the importance of the early detection and treatment of early-life ID to ameliorate its chronic metabolic effects.
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Affiliation(s)
- Jordi Mayneris-Perxachs
- Department of Diabetes, Endocrinology and Nutrition, Josep Trueta University Hospital, Girona, Spain.,Nutrition, Eumetabolism and Health Group, Girona Biomedical Research Institute (IdibGi), Girona, Spain.,Obesity and Nutrition, Madrid, Spain
| | - Wellington Amaral
- Harlow Center for Biological Psychology, University of Wisconsin, Madison, WI, USA
| | - Gabriele R Lubach
- Harlow Center for Biological Psychology, University of Wisconsin, Madison, WI, USA
| | - Mark Lyte
- College of Veterinary Medicine, Iowa State University
| | | | - Joram M Posma
- Department of Metabolism, DigCIBER in Physiopathology of estion and Reproduction, Imperial College London, UK
| | - Christopher L Coe
- Harlow Center for Biological Psychology, University of Wisconsin, Madison, WI, USA
| | - Jonathan R Swann
- Department of Metabolism, DigCIBER in Physiopathology of estion and Reproduction, Imperial College London, UK.,School of Human Development and Health, Faculty of Medicine, University of Southampton, UK.,Department of Neuroscience, Karolinska Institute, Sweden
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15
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Penney N, Barton W, Posma JM, Darzi A, Frost G, Cotter PD, Holmes E, Shanahan F, O'Sullivan O, Garcia-Perez I. Investigating the Role of Diet and Exercise in Gut Microbe-Host Cometabolism. mSystems 2020; 5:e00677-20. [PMID: 33262239 PMCID: PMC7716389 DOI: 10.1128/msystems.00677-20] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 11/04/2020] [Indexed: 12/22/2022] Open
Abstract
We investigated the individual and combined effects of diet and physical exercise on metabolism and the gut microbiome to establish how these lifestyle factors influence host-microbiome cometabolism. Urinary and fecal samples were collected from athletes and less active controls. Individuals were further classified according to an objective dietary assessment score of adherence to healthy dietary habits according to WHO guidelines, calculated from their proton nuclear magnetic resonance (1H-NMR) urinary profiles. Subsequent models were generated comparing extremes of dietary habits, exercise, and the combined effect of both. Differences in metabolic phenotypes and gut microbiome profiles between the two groups were assessed. Each of the models pertaining to diet healthiness, physical exercise, or a combination of both displayed a metabolic and functional microbial signature, with a significant proportion of the metabolites identified as discriminating between the various pairwise comparisons resulting from gut microbe-host cometabolism. Microbial diversity was associated with a combination of high adherence to healthy dietary habits and exercise and was correlated with a distinct array of microbially derived metabolites, including markers of proteolytic activity. Improved control of dietary confounders, through the use of an objective dietary assessment score, has uncovered further insights into the complex, multifactorial relationship between diet, exercise, the gut microbiome, and metabolism. Furthermore, the observation of higher proteolytic activity associated with higher microbial diversity indicates that increased microbial diversity may confer deleterious as well as beneficial effects on the host.IMPORTANCE Improved control of dietary confounders, through the use of an objective dietary assessment score, has uncovered further insights into the complex, multifactorial relationship between diet, exercise, the gut microbiome, and metabolism. Each of the models pertaining to diet healthiness, physical exercise, or a combination of both, displayed a distinct metabolic and functional microbial signature. A significant proportion of the metabolites identified as discriminating between the various pairwise comparisons result from gut microbe-host cometabolism, and the identified interactions have expanded current knowledge in this area. Furthermore, although increased microbial diversity has previously been linked with health, our observation of higher microbial diversity being associated with increased proteolytic activity indicates that it may confer deleterious as well as beneficial effects on the host.
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Affiliation(s)
- N Penney
- Division of Surgery, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - W Barton
- APC Microbiome Ireland, University College Cork, National University of Ireland, Cork, Ireland
- Teagasc Food Research Centre, Moorepark, Co. Cork, Ireland
- Department of Medicine, University College Cork, National University of Ireland, Cork, Ireland
| | - J M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - A Darzi
- Division of Surgery, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - G Frost
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - P D Cotter
- APC Microbiome Ireland, University College Cork, National University of Ireland, Cork, Ireland
- Teagasc Food Research Centre, Moorepark, Co. Cork, Ireland
| | - E Holmes
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - F Shanahan
- APC Microbiome Ireland, University College Cork, National University of Ireland, Cork, Ireland
- Department of Medicine, University College Cork, National University of Ireland, Cork, Ireland
| | - O O'Sullivan
- APC Microbiome Ireland, University College Cork, National University of Ireland, Cork, Ireland
- Teagasc Food Research Centre, Moorepark, Co. Cork, Ireland
| | - I Garcia-Perez
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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16
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Eriksen R, Perez IG, Posma JM, Haid M, Sharma S, Prehn C, Thomas LE, Koivula RW, Bizzotto R, Prehn C, Mari A, Giordano GN, Pavo I, Schwenk JM, De Masi F, Tsirigos KD, Brunak S, Viñuela A, Mahajan A, McDonald TJ, Kokkola T, Rutter F, Teare H, Hansen TH, Fernandez J, Jones A, Jennison C, Walker M, McCarthy MI, Pedersen O, Ruetten H, Forgie I, Bell JD, Pearson ER, Franks PW, Adamski J, Holmes E, Frost G. Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study. EBioMedicine 2020; 58:102932. [PMID: 32763829 PMCID: PMC7406914 DOI: 10.1016/j.ebiom.2020.102932] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 06/18/2020] [Accepted: 07/15/2020] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. METHODS We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. FINDINGS A higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=-0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2. INTERPRETATION Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health. FUNDING This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies.
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Affiliation(s)
- Rebeca Eriksen
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, United Kingdom.
| | - Isabel Garcia Perez
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, United Kingdom
| | - Joram M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom; Health Data Research UK, London, United Kingdom
| | - Mark Haid
- Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany
| | - Sapna Sharma
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany
| | - Cornelia Prehn
- Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany
| | - Louise E Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Robert W Koivula
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Radcliffe Department of Medicine, Oxford, United Kingdom
| | - Roberto Bizzotto
- Institute of Neuroscience - National Research Council, Padova, Italy
| | - Cornelia Prehn
- Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany
| | - Andrea Mari
- Institute of Neuroscience - National Research Council, Padova, Italy
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Federico De Masi
- Department of Health Technology, Technical University of Denmark, Kgs Lyngby and The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Konstantinos D Tsirigos
- Department of Health Technology, Technical University of Denmark, Kgs Lyngby and The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Department of Health Technology, Technical University of Denmark, Kgs Lyngby and The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Ana Viñuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Timothy J McDonald
- Medical School, Exeter, UK NIHR Exeter Clinical Research Facility, University of Exeter
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Femke Rutter
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Institute, Amsterdam UMC, locationVUMC, Amsterdam, Netherlands
| | - Harriet Teare
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Tue H Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Juan Fernandez
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Angus Jones
- Medical School, Exeter, UK NIHR Exeter Clinical Research Facility, University of Exeter
| | - Chris Jennison
- Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
| | - Mark Walker
- Institute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Radcliffe Department of Medicine, Oxford, United Kingdom; Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Hartmut Ruetten
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - Ian Forgie
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology And Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environemental Health (GmbH), Neuherberg, Germany; Lehrstuhl für Experimentelle Genetik, Technische Universität München, 85350 Freising-Weihenstephan, Germany; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
| | - Elaine Holmes
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, United Kingdom
| | - Gary Frost
- Section for Nutrition Research, Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, United Kingdom; NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom.
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17
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Lahiri S, Kim H, Garcia-Perez I, Reza MM, Martin KA, Kundu P, Cox LM, Selkrig J, Posma JM, Zhang H, Padmanabhan P, Moret C, Gulyás B, Blaser MJ, Auwerx J, Holmes E, Nicholson J, Wahli W, Pettersson S. The gut microbiota influences skeletal muscle mass and function in mice. Sci Transl Med 2020; 11:11/502/eaan5662. [PMID: 31341063 DOI: 10.1126/scitranslmed.aan5662] [Citation(s) in RCA: 241] [Impact Index Per Article: 60.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 09/30/2018] [Accepted: 02/11/2019] [Indexed: 12/25/2022]
Abstract
The functional interactions between the gut microbiota and the host are important for host physiology, homeostasis, and sustained health. We compared the skeletal muscle of germ-free mice that lacked a gut microbiota to the skeletal muscle of pathogen-free mice that had a gut microbiota. Compared to pathogen-free mouse skeletal muscle, germ-free mouse skeletal muscle showed atrophy, decreased expression of insulin-like growth factor 1, and reduced transcription of genes associated with skeletal muscle growth and mitochondrial function. Nuclear magnetic resonance spectrometry analysis of skeletal muscle, liver, and serum from germ-free mice revealed multiple changes in the amounts of amino acids, including glycine and alanine, compared to pathogen-free mice. Germ-free mice also showed reduced serum choline, the precursor of acetylcholine, the key neurotransmitter that signals between muscle and nerve at neuromuscular junctions. Reduced expression of genes encoding Rapsyn and Lrp4, two proteins important for neuromuscular junction assembly and function, was also observed in skeletal muscle from germ-free mice compared to pathogen-free mice. Transplanting the gut microbiota from pathogen-free mice into germ-free mice resulted in an increase in skeletal muscle mass, a reduction in muscle atrophy markers, improved oxidative metabolic capacity of the muscle, and elevated expression of the neuromuscular junction assembly genes Rapsyn and Lrp4 Treating germ-free mice with short-chain fatty acids (microbial metabolites) partly reversed skeletal muscle impairments. Our results suggest a role for the gut microbiota in regulating skeletal muscle mass and function in mice.
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Affiliation(s)
- Shawon Lahiri
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden. .,Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Hyejin Kim
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Isabel Garcia-Perez
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Sir Alexander Fleming Building, Imperial College London, London SW72AZ, UK
| | - Musarrat Maisha Reza
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Katherine A Martin
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Parag Kundu
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,Singapore Center for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore, Singapore
| | - Laura M Cox
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Joel Selkrig
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Joram M Posma
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Sir Alexander Fleming Building, Imperial College London, London SW72AZ, UK
| | - Hongbo Zhang
- Laboratory of Integrative and Systems Physiology, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Catherine Moret
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Balázs Gulyás
- Department of Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Martin J Blaser
- Department of Medicine, New York University School of Medicine, New York, NY 10016, USA.,Medical Service, VA New York Harbor Healthcare System, New York, NY 10010, USA
| | - Johan Auwerx
- Laboratory of Integrative and Systems Physiology, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elaine Holmes
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Sir Alexander Fleming Building, Imperial College London, London SW72AZ, UK
| | - Jeremy Nicholson
- Australian National Phenome Center, Murdoch University, WA 6150, Australia
| | - Walter Wahli
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,INRA ToxAlim Integrative Toxicology and Metabolism UMR1331, Chemin de Tournefeuille, Toulouse Cedex, France
| | - Sven Pettersson
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden. .,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,Singapore Center for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore, Singapore
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18
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Posma JM, Garcia-Perez I, Frost G, Aljuraiban GS, Chan Q, Van Horn L, Daviglus M, Stamler J, Holmes E, Elliott P, Nicholson JK. Nutriome-metabolome relationships provide insights into dietary intake and metabolism. ACTA ACUST UNITED AC 2020; 1:426-436. [PMID: 32954362 PMCID: PMC7497842 DOI: 10.1038/s43016-020-0093-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Dietary assessment traditionally relies on self-reported data which are often inaccurate and may result in erroneous diet-disease risk associations. We illustrate how urinary metabolic phenotyping can be used as alternative approach for obtaining information on dietary patterns. We used two multi-pass 24-hr dietary recalls, obtained on two occasions on average three weeks apart, paired with two 24-hr urine collections from 1,848 U.S. individuals; 67 nutrients influenced the urinary metabotype measured with 1H-NMR spectroscopy characterized by 46 structurally identified metabolites. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living U.S. cohort and replicated in an independent U.K. cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers are associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to unhealthy metabolic phenotype and hence give entry points for prevention and intervention strategies.
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Affiliation(s)
- Joram M Posma
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, South Kensington Campus, Imperial College London, SW7 2AZ, U.K.,Health Data Research UK-London, U.K
| | - Isabel Garcia-Perez
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K
| | - Gary Frost
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K
| | - Ghadeer S Aljuraiban
- The Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia.,Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K
| | - Queenie Chan
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K.,MRC Centre for Environment and Health, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K
| | - Linda Van Horn
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, U.S.A
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL 60612
| | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, U.S.A
| | - Elaine Holmes
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K.,UK Dementia Research Institute, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K.,Division of Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia.,The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, WA 6150, Australia
| | - Paul Elliott
- Health Data Research UK-London, U.K.,Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K.,MRC Centre for Environment and Health, School of Public Health, Faculty of Medicine, St. Mary's Campus, Imperial College London, W2 1PG, U.K.,UK Dementia Research Institute, Faculty of Medicine, Hammersmith Campus, Imperial College London, W12 0NN, U.K.,National Institute for Health Research Imperial Biomedical Research Centre, St. Mary's Campus, Imperial College London, W2 1PG, U.K.,British Heart Foundation Centre of Research Excellence at Imperial, Imperial College London, W2 1PG, U.K
| | - Jeremy K Nicholson
- Division of Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, WA 6150, Australia.,The Australian National Phenome Center, Harry Perkins Institute, Murdoch University, WA 6150, Australia
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19
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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20
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Garcia-Perez I, Posma JM, Chambers ES, Mathers JC, Draper J, Beckmann M, Nicholson JK, Holmes E, Frost G. RETRACTED ARTICLE: Dietary metabotype modelling predicts individual responses to dietary interventions. Nat Food 2020; 1:355-364. [PMID: 37128097 DOI: 10.1038/s43016-020-0092-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 05/06/2020] [Indexed: 12/12/2022]
Abstract
Habitual consumption of poor quality diets is linked directly to risk factors for many non-communicable diseases. This has resulted in the vast majority of countries and the World Health Organization developing policies for healthy eating to reduce the prevalence of non-communicable diseases in the population. However, there is mounting evidence of variability in individual metabolic responses to any dietary intervention. We have developed a method for applying a pipeline for understanding interindividual differences in response to diet, based on coupling data from highly controlled dietary studies with deep metabolic phenotyping. In this feasibility study, we create an individual Dietary Metabotype Score (DMS) that embodies interindividual variability in dietary response and captures consequent dynamic changes in concentrations of urinary metabolites. We find an inverse relationship between the DMS and blood glucose concentration. There is also a relationship between the DMS and urinary metabolic energy loss. Furthermore, we use a metabolic entropy approach to visualize individual and collective responses to dietary interventions. Potentially, the DMS offers a method to target and to enhance dietary response at the individual level, thereby reducing the burden of non-communicable diseases at the population level.
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Affiliation(s)
- Isabel Garcia-Perez
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Joram M Posma
- Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Health Data Research UK-London, London, UK
| | - Edward S Chambers
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - John C Mathers
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - John Draper
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - Manfred Beckmann
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - Jeremy K Nicholson
- The Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
| | - Elaine Holmes
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
- The Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
| | - Gary Frost
- Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
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21
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Andreas NJ, Basu Roy R, Gomez-Romero M, Horneffer-van der Sluis V, Lewis MR, Camuzeaux SSM, Jiménez B, Posma JM, Tientcheu L, Egere U, Sillah A, Togun T, Holmes E, Kampmann B. Performance of metabonomic serum analysis for diagnostics in paediatric tuberculosis. Sci Rep 2020; 10:7302. [PMID: 32350385 PMCID: PMC7190829 DOI: 10.1038/s41598-020-64413-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 03/13/2020] [Indexed: 12/31/2022] Open
Abstract
We applied a metabonomic strategy to identify host biomarkers in serum to diagnose paediatric tuberculosis (TB) disease. 112 symptomatic children with presumptive TB were recruited in The Gambia and classified as bacteriologically-confirmed TB, clinically diagnosed TB, or other diseases. Sera were analysed using 1H nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Multivariate data analysis was used to distinguish patients with TB from other diseases. Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves. Model performance was tested in a validation cohort of 36 children from the UK. Data acquired using 1H NMR demonstrated a sensitivity, specificity and Area Under the Curve (AUC) of 69% (95% confidence interval [CI], 56-73%), 83% (95% CI, 73-93%), and 0.78 respectively, and correctly classified 20% of the validation cohort from the UK. The most discriminatory MS data showed a sensitivity of 67% (95% CI, 60-71%), specificity of 86% (95% CI, 75-93%) and an AUC of 0.78, correctly classifying 83% of the validation cohort. Amongst children with presumptive TB, metabolic profiling of sera distinguished bacteriologically-confirmed and clinical TB from other diseases. This novel approach yielded a diagnostic performance for paediatric TB comparable to that of Xpert MTB/RIF and interferon gamma release assays.
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Affiliation(s)
- Nicholas J Andreas
- Centre for International Child Health, Department of Paediatrics, Imperial College London, St. Mary's Hospital, Praed Street, London, W2 1NY, United Kingdom
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Robindra Basu Roy
- Centre for International Child Health, Department of Paediatrics, Imperial College London, St. Mary's Hospital, Praed Street, London, W2 1NY, United Kingdom
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
- The Vaccine Centre, Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Maria Gomez-Romero
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
- Clinical Phenotyping Centre, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Verena Horneffer-van der Sluis
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
| | - Matthew R Lewis
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
- Clinical Phenotyping Centre, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Stephane S M Camuzeaux
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
| | - Beatriz Jiménez
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
- Clinical Phenotyping Centre, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Joram M Posma
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Leopold Tientcheu
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
| | - Uzochukwu Egere
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
| | - Abdou Sillah
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
| | - Toyin Togun
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
- The Vaccine Centre, Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Elaine Holmes
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Beate Kampmann
- Centre for International Child Health, Department of Paediatrics, Imperial College London, St. Mary's Hospital, Praed Street, London, W2 1NY, United Kingdom.
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia.
- The Vaccine Centre, Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom.
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22
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Ocvirk S, Wilson AS, Posma JM, Li JV, Koller KR, Day GM, Flanagan CA, Otto JE, Sacco PE, Sacco FD, Sapp FR, Wilson AS, Newton K, Brouard F, DeLany JP, Behnning M, Appolonia CN, Soni D, Bhatti F, Methé B, Fitch A, Morris A, Gaskins HR, Kinross J, Nicholson JK, Thomas TK, O'Keefe SJD. A prospective cohort analysis of gut microbial co-metabolism in Alaska Native and rural African people at high and low risk of colorectal cancer. Am J Clin Nutr 2020; 111:406-419. [PMID: 31851298 PMCID: PMC6997097 DOI: 10.1093/ajcn/nqz301] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/14/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Alaska Native (AN) people have the world's highest recorded incidence of sporadic colorectal cancer (CRC) (∼91:100,000), whereas rural African (RA) people have the lowest risk (<5:100,000). Previous data supported the hypothesis that diet affected CRC risk through its effects on the colonic microbiota that produce tumor-suppressive or -promoting metabolites. OBJECTIVES We investigated whether differences in these metabolites may contribute to the high risk of CRC in AN people. METHODS A cross-sectional observational study assessed dietary intake from 32 AN and 21 RA healthy middle-aged volunteers before screening colonoscopy. Analysis of fecal microbiota composition by 16S ribosomal RNA gene sequencing and fecal/urinary metabolites by 1H-NMR spectroscopy was complemented with targeted quantification of fecal SCFAs, bile acids, and functional microbial genes. RESULTS Adenomatous polyps were detected in 16 of 32 AN participants, but not found in RA participants. The AN diet contained higher proportions of fat and animal protein and less fiber. AN fecal microbiota showed a compositional predominance of Blautia and Lachnoclostridium, higher microbial capacity for bile acid conversion, and low abundance of some species involved in saccharolytic fermentation (e.g., Prevotellaceae, Ruminococcaceae), but no significant lack of butyrogenic bacteria. Significantly lower concentrations of tumor-suppressive butyrate (22.5 ± 3.1 compared with 47.2 ± 7.3 SEM µmol/g) coincided with significantly higher concentrations of tumor-promoting deoxycholic acid (26.7 ± 4.2 compared with 11 ± 1.9 µmol/g) in AN fecal samples. AN participants had lower quantities of fecal/urinary metabolites than RA participants and metabolite profiles correlated with the abundance of distinct microbial genera in feces. The main microbial and metabolic CRC-associated markers were not significantly altered in AN participants with adenomatous polyps. CONCLUSIONS The low-fiber, high-fat diet of AN people and exposure to carcinogens derived from diet or environment are associated with a tumor-promoting colonic milieu as reflected by the high rates of adenomatous polyps in AN participants.
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Affiliation(s)
- Soeren Ocvirk
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Gastrointestinal Microbiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Annette S Wilson
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joram M Posma
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, London, United Kingdom
| | - Jia V Li
- Section of Nutritional Research, Division of Digestive Diseases, Department of Metabolism, Digestion, and Reproduction, Imperial College, London, United Kingdom
- Centre for Digestive and Gut Health, Institution of Global Health Innovation, Imperial College, London, United Kingdom
| | - Kathryn R Koller
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Gretchen M Day
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Christie A Flanagan
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Jill Evon Otto
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Pam E Sacco
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Frank D Sacco
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Flora R Sapp
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Amy S Wilson
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Keith Newton
- Division of Gastroenterology, University of KwaZulu-Natal, Durban, South Africa
| | - Faye Brouard
- Manguzi Hospital, Manguzi, KwaZulu-Natal, South Africa
| | - James P DeLany
- Division of Endocrinology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Translational Research Institute for Metabolism and Diabetes, Advent Health, Orlando, FL, USA
| | - Marissa Behnning
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Corynn N Appolonia
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Devavrata Soni
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Faheem Bhatti
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Barbara Methé
- Center for Medicine and the Microbiome, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Adam Fitch
- Center for Medicine and the Microbiome, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alison Morris
- Center for Medicine and the Microbiome, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - H Rex Gaskins
- Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - James Kinross
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Jeremy K Nicholson
- Centre for Digestive and Gut Health, Institution of Global Health Innovation, Imperial College, London, United Kingdom
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Timothy K Thomas
- Clinical & Research Services, Community Health Services, Alaska Native Tribal Health Consortium, Anchorage, AK, USA
| | - Stephen J D O'Keefe
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
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23
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Wilson T, Garcia-Perez I, Posma JM, Lloyd AJ, Chambers ES, Tailliart K, Zubair H, Beckmann M, Mathers JC, Holmes E, Frost G, Draper J. Spot and Cumulative Urine Samples Are Suitable Replacements for 24-Hour Urine Collections for Objective Measures of Dietary Exposure in Adults Using Metabolite Biomarkers. J Nutr 2019; 149:1692-1700. [PMID: 31240300 DOI: 10.1093/jn/nxz138] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 04/17/2019] [Accepted: 05/24/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Measurement of multiple food intake exposure biomarkers in urine may offer an objective method for monitoring diet. The potential of spot and cumulative urine samples that have reduced burden on participants as replacements for 24-h urine collections has not been evaluated. OBJECTIVE The aim of this study was to determine the utility of spot and cumulative urine samples for classifying the metabolic profiles of people according to dietary intake when compared with 24-h urine collections in a controlled dietary intervention study. METHODS Nineteen healthy individuals (10 male, 9 female, aged 21-65 y, BMI 20-35 kg/m2) each consumed 4 distinctly different diets, each for 1 wk. Spot urine samples were collected ∼2 h post meals on 3 intervention days/wk. Cumulative urine samples were collected daily over 3 separate temporal periods. A 24-h urine collection was created by combining the 3 cumulative urine samples. Urine samples were analyzed with metabolite fingerprinting by both high-resolution flow infusion electrospray mass spectrometry (FIE-HRMS) and proton nuclear magnetic resonance spectroscopy (1H-NMR). Concentrations of dietary intake biomarkers were measured with liquid chromatography triple quadrupole mass spectrometry and by integration of 1H-NMR data. RESULTS Cross-validation modeling with 1H-NMR and FIE-HRMS data demonstrated the power of spot and cumulative urine samples in predicting dietary patterns in 24-h urine collections. Particularly, there was no significant loss of information when post-dinner (PD) spot or overnight cumulative samples were substituted for 24-h urine collections (classification accuracies of 0.891 and 0.938, respectively). Quantitative analysis of urine samples also demonstrated the relation between PD spot samples and 24-h urines for dietary exposure biomarkers. CONCLUSIONS We conclude that PD spot urine samples are suitable replacements for 24-h urine collections. Alternatively, cumulative samples collected overnight predict similarly to 24-h urine samples and have a lower collection burden for participants.
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Affiliation(s)
- Thomas Wilson
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Isabel Garcia-Perez
- Section of Biomolecular Medicine, Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Joram M Posma
- Section of Biomolecular Medicine, Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Amanda J Lloyd
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Edward S Chambers
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, United Kingdom
| | - Kathleen Tailliart
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Hassan Zubair
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Manfred Beckmann
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - John C Mathers
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Elaine Holmes
- Section of Biomolecular Medicine, Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Gary Frost
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, United Kingdom
| | - John Draper
- Institute of Biological, Environmental, and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
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24
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Boulangé CL, Rood IM, Posma JM, Lindon JC, Holmes E, Wetzels JFM, Deegens JKJ, Kaluarachchi MR. NMR and MS urinary metabolic phenotyping in kidney diseases is fit-for-purpose in the presence of a protease inhibitor. Mol Omics 2019; 15:39-49. [DOI: 10.1039/c8mo00190a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
When using an appropriate data analysis pipeline, protease inhibitor (PI)-containing urine samples are fit-for-purpose for metabolic phenotyping of patients with nephrotic syndrome and proteinuria.
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Affiliation(s)
| | - Ilse M. Rood
- Department of Nephrology
- Radboud University Medical Center
- Nijmegen
- The Netherlands
| | - Joram M. Posma
- Imperial College London
- Division of Computational and Systems Medicine
- Department of Surgery and Cancer
- Faculty of Medicine
- London SW7 2AZ
| | - John C. Lindon
- Metabometrix Ltd
- London SW7 2AZ
- UK
- Imperial College London
- Division of Computational and Systems Medicine
| | - Elaine Holmes
- Metabometrix Ltd
- London SW7 2AZ
- UK
- Imperial College London
- Division of Computational and Systems Medicine
| | - Jack F. M. Wetzels
- Department of Nephrology
- Radboud University Medical Center
- Nijmegen
- The Netherlands
| | - Jeroen K. J. Deegens
- Department of Nephrology
- Radboud University Medical Center
- Nijmegen
- The Netherlands
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25
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Rodriguez-Martinez A, Posma JM, Ayala R, Neves AL, Anwar M, Petretto E, Emanueli C, Gauguier D, Nicholson JK, Dumas ME. MWASTools: an R/bioconductor package for metabolome-wide association studies. Bioinformatics 2018; 34:890-892. [PMID: 28961702 PMCID: PMC6049002 DOI: 10.1093/bioinformatics/btx477] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 07/24/2017] [Indexed: 12/14/2022] Open
Abstract
Summary MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome-wide association studies results. Availability and implementation The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Joram M Posma
- Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK
| | - Rafael Ayala
- Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK
| | - Ana L Neves
- Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK
| | - Maryam Anwar
- Division of Myocardial Function, National Heart and Lung Institute, Imperial College London, UK
| | | | - Costanza Emanueli
- Division of Myocardial Function, National Heart and Lung Institute, Imperial College London, UK.,Bristol Heart Institute, University of Bristol, UK
| | - Dominique Gauguier
- Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK
| | - Jeremy K Nicholson
- Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK
| | - Marc-Emmanuel Dumas
- Department of Surgery and Cancer, Computational and Systems Medicine, Imperial College London, UK
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26
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Rodriguez-Martinez A, Ayala R, Posma JM, Dumas ME. Exploring the Genetic Landscape of Metabolic Phenotypes with MetaboSignal. ACTA ACUST UNITED AC 2018; 61:14.14.1-14.14.13. [DOI: 10.1002/cpbi.41] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Andrea Rodriguez-Martinez
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, South Kensington Campus, Imperial College London; London United Kingdom
| | - Rafael Ayala
- Section of Structural Biology, Department of Medicine, Faculty of Medicine, South Kensington Campus, Imperial College London; London United Kingdom
| | - Joram M. Posma
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, South Kensington Campus, Imperial College London; London United Kingdom
| | - Marc-Emmanuel Dumas
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, South Kensington Campus, Imperial College London; London United Kingdom
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27
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Posma JM, Garcia-Perez I, Ebbels TMD, Lindon JC, Stamler J, Elliott P, Holmes E, Nicholson JK. Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data. J Proteome Res 2018; 17:1586-1595. [PMID: 29457906 PMCID: PMC5891819 DOI: 10.1021/acs.jproteome.7b00879] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.
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Affiliation(s)
| | - Isabel Garcia-Perez
- Investigative Medicine, Department of Medicine, Faculty of Medicine , Imperial College London , W12 0NN London , United Kingdom
| | | | | | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine , Northwestern University , Chicago , Illinois 60611 , United States
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28
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Rodriguez-Martinez A, Ayala R, Posma JM, Neves AL, Gauguier D, Nicholson JK, Dumas ME. MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways. Bioinformatics 2018; 33:773-775. [PMID: 28011775 PMCID: PMC5408820 DOI: 10.1093/bioinformatics/btw697] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 11/03/2016] [Indexed: 12/31/2022] Open
Abstract
Summary MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. Availability and Implementation MetaboSignal is available from Bioconductor: https://bioconductor.org/packages/MetaboSignal/ Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrea Rodriguez-Martinez
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Rafael Ayala
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Joram M Posma
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Ana L Neves
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Dominique Gauguier
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.,Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERMUMR_S 1138, Cordeliers Research Centre, 75006 Paris, France
| | - Jeremy K Nicholson
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Marc-Emmanuel Dumas
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
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29
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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30
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Garcia-Perez I, Posma JM, Gibson R, Chambers ES, Hansen TH, Vestergaard H, Hansen T, Beckmann M, Pedersen O, Elliott P, Stamler J, Nicholson JK, Draper J, Mathers JC, Holmes E, Frost G. Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial. Lancet Diabetes Endocrinol 2017; 5:184-195. [PMID: 28089709 PMCID: PMC5357736 DOI: 10.1016/s2213-8587(16)30419-3] [Citation(s) in RCA: 151] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Revised: 11/08/2016] [Accepted: 11/09/2016] [Indexed: 12/15/2022]
Abstract
BACKGROUND Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, the concentrations of which can be affected by food intake. We hypothesised that metabolic profiles of urine samples developed under controlled feeding conditions reflect dietary intake and can be used to model and classify dietary patterns of free-living populations. METHODS In this randomised, controlled, crossover trial, we recruited healthy volunteers (aged 21-65 years, BMI 20-35 kg/m2) from a database of a clinical research unit in the UK. We developed four dietary interventions with a stepwise variance in concordance with the WHO healthy eating guidelines that aim to prevent non-communicable diseases (increase fruits, vegetables, whole grains, and dietary fibre; decrease fats, sugars, and salt). Participants attended four inpatient stays (72 h each, separated by at least 5 days), during which they were given one dietary intervention. The order of diets was randomly assigned across study visits. Randomisation was done by an independent investigator, with the use of opaque, sealed, sequentially numbered envelopes that each contained one of the four dietary interventions in a random order. Participants and investigators were not masked from the dietary intervention, but investigators analysing the data were masked from the randomisation order. During each inpatient period, urine was collected daily over three timed periods: morning (0900-1300 h), afternoon (1300-1800 h), and evening and overnight (1800-0900 h); 24 h urine samples were obtained by pooling these samples. Urine samples were assessed by proton nuclear magnetic resonance (1H-NMR) spectroscopy, and diet-discriminatory metabolites were identified. We developed urinary metabolite models for each diet and identified the associated metabolic profiles, and then validated the models using data and samples from the INTERMAP UK cohort (n=225) and a healthy-eating Danish cohort (n=66). This study is registered with ISRCTN, number ISRCTN43087333. FINDINGS Between Aug 13, 2013, and May 18, 2014, we contacted 300 people with a letter of invitation. 78 responded, of whom 26 were eligible and invited to attend a health screening. Of 20 eligible participants who were randomised, 19 completed all four 72 h study stays between Oct 2, 2013, and July 29, 2014, and consumed all the food provided. Analysis of 1H-NMR spectroscopy data indicated that urinary metabolic profiles of the four diets were distinct. Significant stepwise differences in metabolite concentrations were seen between diets with the lowest and highest metabolic risks. Application of the derived metabolite models to the validation datasets confirmed the association between urinary metabolic and dietary profiles in the INTERMAP UK cohort (p<0·0001) and the Danish cohort (p<0·0001). INTERPRETATION Urinary metabolite models developed in a highly controlled environment can classify groups of free-living people into consumers of diets associated with lower or higher non-communicable disease risk on the basis of multivariate metabolite patterns. This approach enables objective monitoring of dietary patterns in population settings and enhances the validity of dietary reporting. FUNDING UK National Institute for Health Research and UK Medical Research Council.
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Affiliation(s)
- Isabel Garcia-Perez
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK; Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Joram M Posma
- Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Rachel Gibson
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Edward S Chambers
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Tue H Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Vestergaard
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Manfred Beckmann
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Medical Research Council (MRC)-Public Health England Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; MRC-National Institute for Health Research (NIHR) National Phenome Centre, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Jeremy K Nicholson
- Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK; MRC-National Institute for Health Research (NIHR) National Phenome Centre, Department of Surgery and Cancer, Imperial College London, London, UK
| | - John Draper
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - John C Mathers
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - Elaine Holmes
- Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, UK; MRC-National Institute for Health Research (NIHR) National Phenome Centre, Department of Surgery and Cancer, Imperial College London, London, UK.
| | - Gary Frost
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK.
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31
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Garcia-Perez I, Posma JM, Chambers ES, Nicholson JK, C Mathers J, Beckmann M, Draper J, Holmes E, Frost G. An Analytical Pipeline for Quantitative Characterization of Dietary Intake: Application To Assess Grape Intake. J Agric Food Chem 2016; 64:2423-2431. [PMID: 26909845 DOI: 10.1021/acs.jafc.5b05878] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Lack of accurate dietary assessment in free-living populations requires discovery of new biomarkers reflecting food intake qualitatively and quantitatively to objectively evaluate effects of diet on health. We provide a proof-of-principle for an analytical pipeline to identify quantitative dietary biomarkers. Tartaric acid was identified by nuclear magnetic resonance spectroscopy as a dose-responsive urinary biomarker of grape intake and subsequently quantified in volunteers following a series of 4-day dietary interventions incorporating 0 g/day, 50 g/day, 100 g/day, and 150 g/day of grapes in standardized diets from a randomized controlled clinical trial. Most accurate quantitative predictions of grape intake were obtained in 24 h urine samples which have the strongest linear relationship between grape intake and tartaric acid excretion (r(2) = 0.90). This new methodological pipeline for estimating nutritional intake based on coupling dietary intake information and quantified nutritional biomarkers was developed and validated in a controlled dietary intervention study, showing that this approach can improve the accuracy of estimating nutritional intakes.
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Affiliation(s)
- Isabel Garcia-Perez
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Imperial College London , London W12 0NN, U.K
| | - Joram M Posma
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, U.K
| | - Edward S Chambers
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Imperial College London , London W12 0NN, U.K
| | - Jeremy K Nicholson
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, U.K
| | - John C Mathers
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University , Newcastle upon Tyne NE4 5PL, U.K
| | - Manfred Beckmann
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University , Aberystwyth SY23 3DA, U.K
| | - John Draper
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University , Aberystwyth SY23 3DA, U.K
| | - Elaine Holmes
- Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, U.K
| | - Gary Frost
- Nutrition and Dietetic Research Group, Division of Endocrinology and Metabolism, Imperial College London , London W12 0NN, U.K
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32
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Elliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M, Ebbels TMD, Ueshima H, Zhao L, van Horn L, Daviglus M, Stamler J, Holmes E, Nicholson JK. Urinary metabolic signatures of human adiposity. Sci Transl Med 2015; 7:285ra62. [PMID: 25925681 DOI: 10.1126/scitranslmed.aaa5680] [Citation(s) in RCA: 149] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2014] [Accepted: 04/10/2015] [Indexed: 12/22/2022]
Abstract
Obesity is a major public health problem worldwide. We used 24-hour urinary metabolic profiling by proton ((1)H) nuclear magnetic resonance (NMR) spectroscopy and ion exchange chromatography to characterize the metabolic signatures of adiposity in the U.S. (n = 1880) and UK (n = 444) cohorts of the INTERMAP (International Study of Macro- and Micronutrients and Blood Pressure) epidemiologic study. Metabolic profiling of urine samples collected over two 24-hour time periods 3 weeks apart showed reproducible patterns of metabolite excretion associated with adiposity. Exploratory analysis of the urinary metabolome using (1)H NMR spectroscopy of the U.S. samples identified 29 molecular species, clustered in interconnecting metabolic pathways, that were significantly associated (P = 1.5 × 10(-5) to 2.0 × 10(-36)) with body mass index (BMI); 25 of these species were also found in the UK validation cohort. We found multiple associations between urinary metabolites and BMI including urinary glycoproteins and N-acetyl neuraminate (related to renal function), trimethylamine, dimethylamine, 4-cresyl sulfate, phenylacetylglutamine and 2-hydroxyisobutyrate (gut microbial co-metabolites), succinate and citrate (tricarboxylic acid cycle intermediates), ketoleucine and the ketoleucine/leucine ratio (linked to skeletal muscle mitochondria and branched-chain amino acid metabolism), ethanolamine (skeletal muscle turnover), and 3-methylhistidine (skeletal muscle turnover and meat intake). We mapped the multiple BMI-metabolite relationships as part of an integrated systems network that describes the connectivities between the complex pathway and compartmental signatures of human adiposity.
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Affiliation(s)
- Paul Elliott
- Department of Epidemiology and Biostatistics, Medical Research Council-Public Health England (MRC-PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK.
| | - Joram M Posma
- Department of Epidemiology and Biostatistics, Medical Research Council-Public Health England (MRC-PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK. Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Queenie Chan
- Department of Epidemiology and Biostatistics, Medical Research Council-Public Health England (MRC-PHE) Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK
| | - Isabel Garcia-Perez
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Anisha Wijeyesekera
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Magda Bictash
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Timothy M D Ebbels
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Hirotsugu Ueshima
- Department of Health Science, Shiga University of Medical Science, Otsu 520-2192, Japan
| | - Liancheng Zhao
- Department of Epidemiology, Fu Wai Hospital and Cardiovascular Institute, Chinese Academy of Medical Sciences, Beijing, Beijing 100037, People's Republic of China
| | - Linda van Horn
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Martha Daviglus
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. College of Medicine at Chicago, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Elaine Holmes
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
| | - Jeremy K Nicholson
- Biomolecular Medicine, Division of Computational and Systems Medicine, MRC-National Institute for Health Research (MRC-NIHR) National Phenome Centre, MRC-PHE Centre for Environment and Health, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK.
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33
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O'Keefe SJD, Li JV, Lahti L, Ou J, Carbonero F, Mohammed K, Posma JM, Kinross J, Wahl E, Ruder E, Vipperla K, Naidoo V, Mtshali L, Tims S, Puylaert PGB, DeLany J, Krasinskas A, Benefiel AC, Kaseb HO, Newton K, Nicholson JK, de Vos WM, Gaskins HR, Zoetendal EG. Fat, fibre and cancer risk in African Americans and rural Africans. Nat Commun 2015; 6:6342. [PMID: 25919227 PMCID: PMC4415091 DOI: 10.1038/ncomms7342] [Citation(s) in RCA: 599] [Impact Index Per Article: 66.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2014] [Accepted: 01/20/2015] [Indexed: 12/12/2022] Open
Abstract
Rates of colon cancer are much higher in African Americans (65:100,000) than in rural South Africans (<5:100,000). The higher rates are associated with higher animal protein and fat, and lower fibre consumption, higher colonic secondary bile acids, lower colonic short-chain fatty acid quantities and higher mucosal proliferative biomarkers of cancer risk in otherwise healthy middle-aged volunteers. Here we investigate further the role of fat and fibre in this association. We performed 2-week food exchanges in subjects from the same populations, where African Americans were fed a high-fibre, low-fat African-style diet and rural Africans a high-fat, low-fibre western-style diet, under close supervision. In comparison with their usual diets, the food changes resulted in remarkable reciprocal changes in mucosal biomarkers of cancer risk and in aspects of the microbiota and metabolome known to affect cancer risk, best illustrated by increased saccharolytic fermentation and butyrogenesis, and suppressed secondary bile acid synthesis in the African Americans.
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Affiliation(s)
- Stephen J D O'Keefe
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | - Jia V Li
- Department of Surgery and Cancer and Centre for Digestive and Gut Health, Institution of Global Health Innovation, Imperial College, London SW7 2AZ, UK
| | - Leo Lahti
- 1] Laboratory of Microbiology, Wageningen University, Wageningen 6703 HB, The Netherlands [2] Department of Veterinary Bioscience, University of Helsinki, Helsinki, Finland
| | - Junhai Ou
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | - Franck Carbonero
- University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, USA
| | - Khaled Mohammed
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | - Joram M Posma
- Department of Surgery and Cancer and Centre for Digestive and Gut Health, Institution of Global Health Innovation, Imperial College, London SW7 2AZ, UK
| | - James Kinross
- Department of Surgery and Cancer and Centre for Digestive and Gut Health, Institution of Global Health Innovation, Imperial College, London SW7 2AZ, UK
| | - Elaine Wahl
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | - Elizabeth Ruder
- Division of Sports Medicine and Nutrition, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | - Kishore Vipperla
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | | | | | - Sebastian Tims
- Laboratory of Microbiology, Wageningen University, Wageningen 6703 HB, The Netherlands
| | - Philippe G B Puylaert
- Laboratory of Microbiology, Wageningen University, Wageningen 6703 HB, The Netherlands
| | - James DeLany
- Division of Endocrinology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | - Alyssa Krasinskas
- Division of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | - Ann C Benefiel
- University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, USA
| | - Hatem O Kaseb
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
| | - Keith Newton
- University of KwaZulu-Natal, Durban, South Africa
| | - Jeremy K Nicholson
- Department of Surgery and Cancer and Centre for Digestive and Gut Health, Institution of Global Health Innovation, Imperial College, London SW7 2AZ, UK
| | - Willem M de Vos
- 1] Laboratory of Microbiology, Wageningen University, Wageningen 6703 HB, The Netherlands [2] Department of Veterinary Bioscience, University of Helsinki, Helsinki, Finland [3] RPU Immunolbiology, Department of Bacteriology and Immunology, University of Helsinki, Helsinki 00014, Finland
| | - H Rex Gaskins
- University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, USA
| | - Erwin G Zoetendal
- Laboratory of Microbiology, Wageningen University, Wageningen 6703 HB, The Netherlands
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Lamour SD, Veselkov KA, Posma JM, Giraud E, Rogers ME, Croft S, Marchesi JR, Holmes E, Seifert K, Saric J. Metabolic, Immune, and Gut Microbial Signals Mount a Systems Response to Leishmania major Infection. J Proteome Res 2014; 14:318-29. [DOI: 10.1021/pr5008202] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Sabrina D. Lamour
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Kirill A. Veselkov
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Joram M. Posma
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Emilie Giraud
- Department of Immunology and Infection, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Matthew E. Rogers
- Department of Disease Control, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Simon Croft
- Department of Immunology and Infection, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Julian R. Marchesi
- Cardiff
School of Biosciences, Division of Microbiology, Cardiff University, Museum Avenue, Cardiff, CF10 3AT, United Kingdom
- Centre
for Digestive and Gut Health, Imperial College London, Exhibition Road, London, SW7 2AZ, United Kingdom
| | - Elaine Holmes
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Karin Seifert
- Department of Immunology and Infection, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Jasmina Saric
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
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35
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Villaseñor A, Garcia-Perez I, Garcia A, Posma JM, Fernández-López M, Nicholas AJ, Modi N, Holmes E, Barbas C. Breast Milk Metabolome Characterization in a Single-Phase Extraction, Multiplatform Analytical Approach. Anal Chem 2014; 86:8245-52. [DOI: 10.1021/ac501853d] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Alma Villaseñor
- Centre
for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte, 28668 Madrid, Spain
| | - Isabel Garcia-Perez
- Computational
and Systems Medicine, Department of Surgery and Cancer, Faculty of
Medicine, Imperial College London, London SW7 2AZ, United Kingdom
- Nutrition
and Dietetic Research Group, Division of Endocrinology and Metabolism,
Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
| | - Antonia Garcia
- Centre
for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte, 28668 Madrid, Spain
| | - Joram M. Posma
- Computational
and Systems Medicine, Department of Surgery and Cancer, Faculty of
Medicine, Imperial College London, London SW7 2AZ, United Kingdom
| | - Mariano Fernández-López
- Escuela
Politécnica Superior, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte, 28668 Madrid, Spain
| | - Andreas J. Nicholas
- Section of Neonatal Medicine, Department of Medicine, Chelsea & Westminster Hospital Campus, Imperial College London, London SW10 9NH, United Kingdom
| | - Neena Modi
- Section of Neonatal Medicine, Department of Medicine, Chelsea & Westminster Hospital Campus, Imperial College London, London SW10 9NH, United Kingdom
| | - Elaine Holmes
- Computational
and Systems Medicine, Department of Surgery and Cancer, Faculty of
Medicine, Imperial College London, London SW7 2AZ, United Kingdom
| | - Coral Barbas
- Centre
for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, Campus Monteprincipe, Boadilla del Monte, 28668 Madrid, Spain
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Posma JM, Robinette SL, Holmes E, Nicholson JK. MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG. ACTA ACUST UNITED AC 2013; 30:893-5. [PMID: 24177720 PMCID: PMC3957072 DOI: 10.1093/bioinformatics/btt612] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Summary: MetaboNetworks is a tool to create custom sub-networks in Matlab using main reaction pairs as defined by the Kyoto Encyclopaedia of Genes and Genomes and can be used to explore transgenomic interactions, for example mammalian and bacterial associations. It calculates the shortest path between a set of metabolites (e.g. biomarkers from a metabonomic study) and plots the connectivity between metabolites as links in a network graph. The resulting graph can be edited and explored interactively. Furthermore, nodes and edges in the graph are linked to the Kyoto Encyclopaedia of Genes and Genomes compound and reaction pair web pages. Availability and implementation: MetaboNetworks is available from http://www.mathworks.com/matlabcentral/fileexchange/42684. Contact:jmp111@ic.ac.uk or j.nicholson@imperial.ac.uk Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Joram M Posma
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
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Ismail NA, Posma JM, Frost G, Holmes E, Garcia-Perez I. The role of metabonomics as a tool for augmenting nutritional information in epidemiological studies. Electrophoresis 2013; 34:2776-86. [PMID: 23893902 DOI: 10.1002/elps.201300066] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 07/04/2013] [Accepted: 07/12/2013] [Indexed: 11/07/2022]
Abstract
Most chronic diseases have been demonstrated to have a link to nutrition. Within food and nutritional research there is a major driver to understand the relationship between diet and disease in order to improve health of individuals. However, the lack of accurate dietary intake assessment in free-living populations, makes accurate estimation of how diet is associated with disease risk difficulty. Thus, there is a pressing need to find solutions to the inaccuracy of dietary reporting. Metabolic profiling of urine or plasma can provide an unbiased approach to characterizing dietary intake and various high-throughput analytical platforms have been used in order to implement targeted and nontargeted assays in nutritional clinical trials and nutritional epidemiology studies. This review describes first the challenges presented in interpreting the relationship between diet and health within individual and epidemiological frameworks. Second, we aim to explore how metabonomics can benefit different types of nutritional studies and discuss the critical importance of selecting appropriate analytical techniques in these studies. Third, we propose a strategy capable of providing accurate assessment of food intake within an epidemiological framework in order establish accurate associations between diet and health.
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Affiliation(s)
- Nurhafzan A Ismail
- Division of Endocrinology and Metabolism, Nutrition and Dietetic Research Group, Imperial College London, London, United Kingdom
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Posma JM, Garcia-Perez I, De Iorio M, Lindon JC, Elliott P, Holmes E, Ebbels TMD, Nicholson JK. Subset optimization by reference matching (STORM): an optimized statistical approach for recovery of metabolic biomarker structural information from 1H NMR spectra of biofluids. Anal Chem 2012; 84:10694-701. [PMID: 23151027 DOI: 10.1021/ac302360v] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
We describe a new multivariate statistical approach to recover metabolite structure information from multiple (1)H NMR spectra in population sample sets. Subset optimization by reference matching (STORM) was developed to select subsets of (1)H NMR spectra that contain specific spectroscopic signatures of biomarkers differentiating between different human populations. STORM aims to improve the visualization of structural correlations in spectroscopic data by using these reduced spectral subsets containing smaller numbers of samples than the number of variables (n ≪ p). We have used statistical shrinkage to limit the number of false positive associations and to simplify the overall interpretation of the autocorrelation matrix. The STORM approach has been applied to findings from an ongoing human metabolome-wide association study on body mass index to identify a biomarker metabolite present in a subset of the population. Moreover, we have shown how STORM improves the visualization of more abundant NMR peaks compared to a previously published method (statistical total correlation spectroscopy, STOCSY). STORM is a useful new tool for biomarker discovery in the "omic" sciences that has widespread applicability. It can be applied to any type of data, provided that there is interpretable correlation among variables, and can also be applied to data with more than one dimension (e.g., 2D NMR spectra).
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Affiliation(s)
- Joram M Posma
- Section of Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London SW7 2AZ, United Kingdom
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Garcia-Perez I, Villaseñor A, Wijeyesekera A, Posma JM, Jiang Z, Stamler J, Aronson P, Unwin R, Barbas C, Elliott P, Nicholson J, Holmes E. Urinary metabolic phenotyping the slc26a6 (chloride-oxalate exchanger) null mouse model. J Proteome Res 2012; 11:4425-35. [PMID: 22594923 DOI: 10.1021/pr2012544] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The prevalence of renal stone disease is increasing, although it remains higher in men than in women when matched for age. While still somewhat controversial, several studies have reported an association between renal stone disease and hypertension, but this may be confounded by a shared link with obesity. However, independent of obesity, hyperoxaluria has been shown to be associated with hypertension in stone-formers, and the most common type of renal stone is composed of calcium oxalate. The chloride-oxalate exchanger slc26a6 (also known as CFEX or PAT-1), located in the renal proximal tubule, was originally thought to have an important role in sodium homeostasis and thereby blood pressure control, but it has recently been shown to have a key function in oxalate balance by mediating oxalate secretion in the gut. We have applied two orthogonal analytical platforms (NMR spectroscopy and capillary electrophoresis with UV detection) in parallel to characterize the urinary metabolic signatures related to the loss of the renal chloride-oxalate exchanger in slc26a6 null mice. Clear metabolic differentiation between the urinary profiles of the slc26a6 null and the wild type mice were observed using both methods, with the combination of NMR and CE-UV providing extensive coverage of the urinary metabolome. Key discriminating metabolites included oxalate, m-hydroxyphenylpropionylsulfate (m-HPPS), trimethylamine-N-oxide, glycolate and scyllo-inositol (higher in slc26a6 null mice) and hippurate, taurine, trimethylamine, and citrate (lower in slc26a6 null mice). In addition to the reduced efficiency of anion transport, several of these metabolites (hippurate, m-HPPS, methylamines) reflect alteration in gut microbial cometabolic activities. Gender-related metabotypes were also observed in both wild type and slc26a6 null groups. Urinary metabolites that showed a sex-specific pattern included trimethylamine, trimethylamine-N-oxide, citrate, spermidine, guanidinoacetate, and 2-oxoisocaproate. The gender-dependent metabolic expression of the consequences of slc26a6 deletion might have relevance to the difference in prevalence of renal stone formation in men and women. The different composition of microbial metabolites in the slc26a6 null mice is consistent with the fact that the slc26a6 transporter is found in a range of tissues, including the kidney and intestine, and provides further evidence for the "long reach" of the microbiota in physiological and pathological processes.
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Affiliation(s)
- Isabel Garcia-Perez
- Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
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