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Liu D, Nagana Gowda GA, Jiang Z, Alemdjrodo K, Zhang M, Zhang D, Raftery D. Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts. Proc Natl Acad Sci U S A 2024; 121:e2307430121. [PMID: 38359289 PMCID: PMC10895372 DOI: 10.1073/pnas.2307430121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 12/05/2023] [Indexed: 02/17/2024] Open
Abstract
Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.
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Affiliation(s)
- Danni Liu
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - G. A. Nagana Gowda
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA98109
| | - Zhongli Jiang
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - Kangni Alemdjrodo
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - Min Zhang
- Department of Statistics, Purdue University, West Lafayette, IN47907
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA92697
| | - Dabao Zhang
- Department of Statistics, Purdue University, West Lafayette, IN47907
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA92697
| | - Daniel Raftery
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA98109
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2
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Nagana Gowda GA, Pascua V, Lusk JA, Hong NN, Guo L, Dong J, Sweet IR, Raftery D. Monitoring live mitochondrial metabolism in real-time using NMR spectroscopy. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:718-727. [PMID: 36882950 PMCID: PMC10483017 DOI: 10.1002/mrc.5341] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/03/2023] [Accepted: 03/03/2023] [Indexed: 06/18/2023]
Abstract
Investigation of mitochondrial metabolism is gaining increased interest owing to the growing recognition of the role of mitochondria in health and numerous diseases. Studies of isolated mitochondria promise novel insights into the metabolism devoid of confounding effects from other cellular organelles such as cytoplasm. This study describes the isolation of mitochondria from mouse skeletal myoblast cells (C2C12) and the investigation of live mitochondrial metabolism in real-time using isotope tracer-based NMR spectroscopy. [3-13 C1 ]pyruvate was used as the substrate to monitor the dynamic changes of the downstream metabolites in mitochondria. The results demonstrate an intriguing phenomenon, in which lactate is produced from pyruvate inside the mitochondria and the results were confirmed by treating mitochondria with an inhibitor of mitochondrial pyruvate carrier (UK5099). Lactate is associated with health and numerous diseases including cancer and, to date, it is known to occur only in the cytoplasm. The insight that lactate is also produced inside mitochondria opens avenues for exploring new pathways of lactate metabolism. Further, experiments performed using inhibitors of the mitochondrial respiratory chain, FCCP and rotenone, show that [2-13 C1 ]acetyl coenzyme A, which is produced from [3-13 C1 ]pyruvate and acts as a primary substrate for the tricarboxylic acid cycle in mitochondria, exhibits a remarkable sensitivity to the inhibitors. These results offer a direct approach to visualize mitochondrial respiration through altered levels of the associated metabolites.
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Affiliation(s)
- G. A. Nagana Gowda
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
- Mitochondria and Metabolism Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
| | - Vadim Pascua
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
- Mitochondria and Metabolism Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
| | - John A. Lusk
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
- Mitochondria and Metabolism Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
| | - Natalie N. Hong
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
- Mitochondria and Metabolism Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
| | - Lin Guo
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
| | - Jiyang Dong
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
- Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Ian R. Sweet
- Department of Medicine, University of Washington, Seattle, Washington 98109, USA
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
- Mitochondria and Metabolism Center, Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, USA
- Fred Hutchinson Cancer Center, Seattle, Washington 98109, USA
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3
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Navarro SL, Nagana Gowda GA, Bettcher LF, Pepin R, Nguyen N, Ellenberger M, Zheng C, Tinker LF, Prentice RL, Huang Y, Yang T, Tabung FK, Chan Q, Loo RL, Liu S, Wactawski-Wende J, Lampe JW, Neuhouser ML, Raftery D. Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women. Metabolites 2023; 13:metabo13040514. [PMID: 37110172 PMCID: PMC10143141 DOI: 10.3390/metabo13040514] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 04/07/2023] Open
Abstract
Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.
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Affiliation(s)
- Sandi L. Navarro
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - G. A. Nagana Gowda
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Lisa F. Bettcher
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Robert Pepin
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Natalie Nguyen
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Mathew Ellenberger
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
| | - Cheng Zheng
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Lesley F. Tinker
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Ross L. Prentice
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Ying Huang
- Biostatistics Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Tao Yang
- School of Public Health, Xinjiang Medical University, Urumqi 830011, China
| | - Fred K. Tabung
- Department of Internal Medicine, Division of Medical Oncology, College of Medicine and Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Queenie Chan
- School of Public Health, Imperial College of London, London SW7 2AZ, UK
| | - Ruey Leng Loo
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Murdoch, WA 6150, Australia
| | - Simin Liu
- Center for Global Cardiometabolic Health, Department of Epidemiology, School of Public Health, Providence, RI 02912, USA
- Department of Medicine and Surgery, Alpert School of Medicine, Brown University, Providence, RI 02903, USA
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY 14214, USA
| | - Johanna W. Lampe
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Marian L. Neuhouser
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Daniel Raftery
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
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Harvey N, Takis PG, Lindon JC, Li JV, Jiménez B. Optimization of Diffusion-Ordered NMR Spectroscopy Experiments for High-Throughput Automation in Human Metabolic Phenotyping. Anal Chem 2023; 95:3147-3152. [PMID: 36720172 PMCID: PMC9933041 DOI: 10.1021/acs.analchem.2c04066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 01/19/2023] [Indexed: 02/02/2023]
Abstract
The diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY) experiment allows the calculation of diffusion coefficient values of metabolites in complex mixtures. However, this experiment has not yet been broadly used for metabolic profiling due to lack of a standardized protocol. Here we propose a pipeline for the DOSY experimental setup and data processing in metabolic phenotyping studies. Due to the complexity of biological samples, three experiments (a standard DOSY, a relaxation-edited DOSY, and a diffusion-edited DOSY) have been optimized to provide DOSY metabolic profiles with peak-picked diffusion coefficients for over 90% of signals visible in the one-dimensional 1H general biofluid profile in as little as 3 min 36 s. The developed parameter sets and tools are straightforward to implement and can facilitate the use of DOSY for metabolic profiling of human blood plasma and urine samples.
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Affiliation(s)
- Nikita Harvey
- Section
of Bioanalytical Chemistry, Division of Systems Medicine, Department
of Metabolism, Digestion and Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital Campus, London W12 0NN, U.K.
- National
Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, , IRDB Building, Hammersmith
Campus, London W12 0NN, U.K.
| | - Panteleimon G Takis
- Section
of Bioanalytical Chemistry, Division of Systems Medicine, Department
of Metabolism, Digestion and Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital Campus, London W12 0NN, U.K.
- National
Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, , IRDB Building, Hammersmith
Campus, London W12 0NN, U.K.
| | - John C Lindon
- Section
of Biomolecular Medicine, Division of Systems Medicine, Department
of Metabolism, Digestion and Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital Campus, London W12 0NN, U.K.
| | - Jia V Li
- Section
of Nutrition, Division of Digestive Diseases, Department of Metabolism,
Digestion and Reproduction, Imperial College
London, Commonwealth Building, Hammersmith Hospital Campus, London W12 0NN, U.K.
| | - Beatriz Jiménez
- Section
of Bioanalytical Chemistry, Division of Systems Medicine, Department
of Metabolism, Digestion and Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital Campus, London W12 0NN, U.K.
- National
Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, , IRDB Building, Hammersmith
Campus, London W12 0NN, U.K.
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Trifonova OP, Maslov DL, Balashova EE, Lichtenberg S, Lokhov PG. Potential Plasma Metabolite Biomarkers of Diabetic Nephropathy: Untargeted Metabolomics Study. J Pers Med 2022; 12:1889. [PMID: 36422065 PMCID: PMC9692474 DOI: 10.3390/jpm12111889] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/04/2022] [Accepted: 11/09/2022] [Indexed: 09/21/2023] Open
Abstract
Diabetic nephropathy (DN) is one of the specific complications of diabetes mellitus and one of the leading kidney-related disorders, often requiring renal replacement therapy. Currently, the tests commonly used for the diagnosis of DN, albuminuria (AU) and glomerular filtration rate (GFR), have limited sensitivity and specificity and can usually be noted when typical morphological changes in the kidney have already been manifested. That is why the extreme urgency of the problem of early diagnosis of this disease exists. The untargeted metabolomics analysis of blood plasma samples from 80 patients with type 1 diabetes and early and late stages of DN according to GFR was performed using direct injection mass spectrometry and bioinformatics analysis for diagnosing signatures construction. Among the dysregulated metabolites, combinations of 15 compounds, including amino acids and derivatives, monosaccharides, organic acids, and uremic toxins were selected for signatures for DN diagnosis. The selected metabolite combinations have shown high performance for diagnosing of DN, especially for the late stage (up to 99%). Despite the metabolite signature determined for the early stage of DN being characterized by a diagnostic performance of 81%, these metabolites as potential biomarkers might be useful in the evaluation of treatment of the disease, especially at early stages that may reduce the risk of kidney failure development.
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Affiliation(s)
- Oxana P. Trifonova
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Dmitry L. Maslov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Elena E. Balashova
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
| | - Steven Lichtenberg
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
- Metabometrics, Inc., 651 N Broad Street, Suite 205 #1370, Middletown, DE 19709, USA
| | - Petr G. Lokhov
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia
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6
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Abstract
Frailty is a complex syndrome affecting a growing sector of the global population as medical developments have advanced human mortality rates across the world. Our current understanding of frailty is derived from studies conducted in the laboratory as well as the clinic, which have generated largely phenotypic information. Far fewer studies have uncovered biological underpinnings driving the onset and progression of frailty, but the stage is set to advance the field with preclinical and clinical assessment tools, multiomics approaches together with physiological and biochemical methodologies. In this article, we provide comprehensive coverage of topics regarding frailty assessment, preclinical models, interventions, and challenges as well as clinical frameworks and prevalence. We also identify central biological mechanisms that may be at play including mitochondrial dysfunction, epigenetic alterations, and oxidative stress that in turn, affect metabolism, stress responses, and endocrine and neuromuscular systems. We review the role of metabolic syndrome, insulin resistance and visceral obesity, focusing on glucose homeostasis, adenosine monophosphate-activated protein kinase (AMPK), mammalian target of rapamycin (mTOR), and nicotinamide adenine dinucleotide (NAD+ ) as critical players influencing the age-related loss of health. We further focus on how immunometabolic dysfunction associates with oxidative stress in promoting sarcopenia, a key contributor to slowness, weakness, and fatigue. We explore the biological mechanisms involved in stem cell exhaustion that affect regeneration and may contribute to the frailty-associated decline in resilience and adaptation to stress. Together, an overview of the interplay of aging biology with genetic, lifestyle, and environmental factors that contribute to frailty, as well as potential therapeutic targets to lower risk and slow the progression of ongoing disease is covered. © 2022 American Physiological Society. Compr Physiol 12:1-46, 2022.
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Affiliation(s)
- Laís R. Perazza
- Department of Physical Therapy and Athletic Training, Boston University, Boston, Massachusetts, USA
| | - Holly M. Brown-Borg
- Department of Biomedical Sciences, University of North Dakota, Grand Forks, North Dakota, USA
| | - LaDora V. Thompson
- Department of Physical Therapy and Athletic Training, Boston University, Boston, Massachusetts, USA
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7
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De Simone G, Balducci C, Forloni G, Pastorelli R, Brunelli L. Hippuric acid: Could became a barometer for frailty and geriatric syndromes? Ageing Res Rev 2021; 72:101466. [PMID: 34560280 DOI: 10.1016/j.arr.2021.101466] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 09/07/2021] [Accepted: 09/17/2021] [Indexed: 12/11/2022]
Abstract
Aging is a natural biological event that has some downsides such as increased frailty, decline in cognitive and physical functions leading to chronical diseases, and lower quality of life. There is therefore a pressing need of reliable biomarkers to identify populations at risk of developing age-associated syndromes in order to improve their quality of life, promote healthy ageing and a more appropriate clinical management, when needed. Here we discuss the importance of hippuric acid, an endogenous co-metabolite, as a possible hallmark of human aging and age-related diseases, summarizing the scientific literature over the last years. Hippuric acid, the glycine conjugate of benzoic acid, derives from the catabolism by means of intestinal microflora of dietary polyphenols found in plant-based foods (e.g. fruits, vegetables, tea and coffee). In healthy conditions hippuric acid levels in blood and/or urine rise significantly during aging while its excretion drops in conditions related with aging, including cognitive impairments, rheumatic diseases, sarcopenia and hypomobility. This literature highlights the utility of hippuric acid in urine and plasma as a plausible hallmark of frailty, related to low fruit and vegetable intake and changes in gut microflora.
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Affiliation(s)
- Giulia De Simone
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Claudia Balducci
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | | | - Laura Brunelli
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
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Comte B, Monnerie S, Brandolini-Bunlon M, Canlet C, Castelli F, Chu-Van E, Colsch B, Fenaille F, Joly C, Jourdan F, Lenuzza N, Lyan B, Martin JF, Migné C, Morais JA, Pétéra M, Poupin N, Vinson F, Thevenot E, Junot C, Gaudreau P, Pujos-Guillot E. Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men. EBioMedicine 2021; 69:103440. [PMID: 34161887 PMCID: PMC8237302 DOI: 10.1016/j.ebiom.2021.103440] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 05/20/2021] [Accepted: 06/01/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Metabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis. METHODS A case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men. FINDINGS We characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…). INTERPRETATION These findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS. FUNDING The NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). The actual NuAge Database and Biobank, containing data and biologic samples of 1,753 NuAge participants (from the initial 1,793 participants), are supported by the Fonds de recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging, a thematic network funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frost Chair funded by La Fondation de l'Université de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the metaboHUB French infrastructure (ANR-INBS-0010). All authors had full access to the full data in the study and accept responsibility to submit for publication.
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Affiliation(s)
- Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Stéphanie Monnerie
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Marion Brandolini-Bunlon
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Cécile Canlet
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Florence Castelli
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Emeline Chu-Van
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Benoit Colsch
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - François Fenaille
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Charlotte Joly
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Fabien Jourdan
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Natacha Lenuzza
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Bernard Lyan
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Jean-François Martin
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Carole Migné
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - José A Morais
- Division de Gériatrie, McGill University, Center de recherche du Center universitaire de santé McGill, Montreal, Canada
| | - Mélanie Pétéra
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nathalie Poupin
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Florence Vinson
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Etienne Thevenot
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Christophe Junot
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Pierrette Gaudreau
- Center de Recherche du Center hospitalier de l'Université de Montréal, Montreal, Canada; Département de médecine, Université de Montréal, Montreal, Canada
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France.
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9
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Lawler NG, Gray N, Kimhofer T, Boughton B, Gay M, Yang R, Morillon AC, Chin ST, Ryan M, Begum S, Bong SH, Coudert JD, Edgar D, Raby E, Pettersson S, Richards T, Holmes E, Whiley L, Nicholson JK. Systemic Perturbations in Amine and Kynurenine Metabolism Associated with Acute SARS-CoV-2 Infection and Inflammatory Cytokine Responses. J Proteome Res 2021; 20:2796-2811. [PMID: 33724837 PMCID: PMC7986977 DOI: 10.1021/acs.jproteome.1c00052] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Indexed: 01/06/2023]
Abstract
We performed quantitative metabolic phenotyping of blood plasma in parallel with cytokine/chemokine analysis from participants who were either SARS-CoV-2 (+) (n = 10) or SARS-CoV-2 (-) (n = 49). SARS-CoV-2 positivity was associated with a unique metabolic phenotype and demonstrated a complex systemic response to infection, including severe perturbations in amino acid and kynurenine metabolic pathways. Nine metabolites were elevated in plasma and strongly associated with infection (quinolinic acid, glutamic acid, nicotinic acid, aspartic acid, neopterin, kynurenine, phenylalanine, 3-hydroxykynurenine, and taurine; p < 0.05), while four metabolites were lower in infection (tryptophan, histidine, indole-3-acetic acid, and citrulline; p < 0.05). This signature supports a systemic metabolic phenoconversion following infection, indicating possible neurotoxicity and neurological disruption (elevations of 3-hydroxykynurenine and quinolinic acid) and liver dysfunction (reduction in Fischer's ratio and elevation of taurine). Finally, we report correlations between the key metabolite changes observed in the disease with concentrations of proinflammatory cytokines and chemokines showing strong immunometabolic disorder in response to SARS-CoV-2 infection.
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Affiliation(s)
- Nathan G. Lawler
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
| | - Nicola Gray
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
| | - Torben Kimhofer
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
| | - Berin Boughton
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
| | - Melvin Gay
- Bruker Pty Ltd., Preston,
VIC 3072, Australia
| | - Rongchang Yang
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
| | - Aude-Claire Morillon
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
| | - Sung-Tong Chin
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
| | - Monique Ryan
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
| | - Sofina Begum
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
- Department of Metabolism Digestion and Reproduction,
Faculty of Medicine, Imperial College London, Sir Alexander
Fleming Building, South Kensington, London SW7 2AZ, U.K.
| | - Sze How Bong
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
| | - Jerome D. Coudert
- Centre for Molecular Medicine & Innovative
Therapeutics, Murdoch University, Perth, WA 6150,
Australia
| | - Dale Edgar
- State Adult Burn Unit, Fiona Stanley
Hospital, Murdoch, WA 6150, Australia
- Burn Injury Research Node, The University of
Notre Dame, Fremantle, WA 6160, Australia
- Fiona Wood Foundation,
Murdoch, WA 6150, Australia
| | - Edward Raby
- Department of Microbiology, PathWest
Laboratory Medicine, Perth, WA 6009, Australia
- Department of Infectious Diseases, Fiona
Stanley Hospital, Perth, WA 6150, Australia
| | - Sven Pettersson
- Singapore National Neuro Science
Centre, Singapore Mandalay Road, Singapore 308232,
Singapore
- Lee Kong Chian School of Medicine,
Nanyang Technological University, Mandalay Road, Singapore
308232, Singapore
- Department of Life Science Centre,
Sunway University, 55100 Kuala Lumpur,
Malaysia
| | - Toby Richards
- Medical School, Faculty of Health and Medical
Sciences, University of Western Australia, Nedlands, WA 6009,
Australia
| | - Elaine Holmes
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
- Department of Metabolism Digestion and Reproduction,
Faculty of Medicine, Imperial College London, Sir Alexander
Fleming Building, South Kensington, London SW7 2AZ, U.K.
| | - Luke Whiley
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
- Perron Institute for Neurological and
Translational Science, Nedlands, WA 6009,
Australia
| | - Jeremy K. Nicholson
- Australian National Phenome Centre, Computational and
Systems Medicine, Health Futures Institute, Murdoch University,
Harry Perkins Building, Perth, WA 6150, Australia
- Medical School, Faculty of Health and Medical
Sciences, University of Western Australia, Nedlands, WA 6009,
Australia
- Institute of Global Health Innovation,
Imperial College London, Level 1, Faculty Building South
Kensington Campus, London SW7 2AZ, U.K.
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10
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Nalbantoglu S, Karadag A. Metabolomics bridging proteomics along metabolites/oncometabolites and protein modifications: Paving the way toward integrative multiomics. J Pharm Biomed Anal 2021; 199:114031. [PMID: 33857836 DOI: 10.1016/j.jpba.2021.114031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 03/02/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023]
Abstract
Systems biology adopted functional and integrative multiomics approaches enable to discover the whole set of interacting regulatory components such as genes, transcripts, proteins, metabolites, and metabolite dependent protein modifications. This interactome build up the midpoint of protein-protein/PTM, protein-DNA/RNA, and protein-metabolite network in a cell. As the key drivers in cellular metabolism, metabolites are precursors and regulators of protein post-translational modifications [PTMs] that affect protein diversity and functionality. The precisely orchestrated core pattern of metabolic networks refer to paradigm 'metabolites regulate PTMs, PTMs regulate enzymes, and enzymes modulate metabolites' through a multitude of feedback and feed-forward pathway loops. The concept represents a flawless PTM-metabolite-enzyme(protein) regulomics underlined in reprogramming cancer metabolism. Immense interconnectivity of those biomolecules in their spectacular network of intertwined metabolic pathways makes integrated proteomics and metabolomics an excellent opportunity, and the central component of integrative multiomics framework. It will therefore be of significant interest to integrate global proteome and PTM-based proteomics with metabolomics to achieve disease related altered levels of those molecules. Thereby, present update aims to highlight role and analysis of interacting metabolites/oncometabolites, and metabolite-regulated PTMs loop which may function as translational monitoring biomarkers along the reprogramming continuum of oncometabolism.
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Affiliation(s)
- Sinem Nalbantoglu
- TUBITAK Marmara Research Center, Gene Engineering and Biotechnology Institute, Molecular, Oncology Laboratory, Gebze, Kocaeli, Turkey.
| | - Abdullah Karadag
- TUBITAK Marmara Research Center, Gene Engineering and Biotechnology Institute, Molecular, Oncology Laboratory, Gebze, Kocaeli, Turkey
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11
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Trifonova OP, Maslov DL, Balashova EE, Lokhov PG. Mass spectrometry-based metabolomics diagnostics - myth or reality? Expert Rev Proteomics 2021; 18:7-12. [PMID: 33653222 DOI: 10.1080/14789450.2021.1893695] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
ABSTACTIntroduction: Metabolomics, one of the most high-promising technologies, is the most recently developed post-genomics discipline for developing new diagnostic tests for future implementation in medicine. More than 2,000 scientific papers, using mass spectrometry-based (MS-based) metabolomics analysis for human disease diagnostics, have been published during the past two decades, and almost every metabolomics study shows high diagnostic accuracy. However, despite the great results and promising perspectives, there are currently no diagnostic tests based on metabolomics that have been approved and introduced into clinics.Areas covered: In this report, the advantages and challenges of MS-based metabolomics are discussed with a focus on its developing role in diagnostics, and the current trends in implementing metabolomics diagnostics in the clinic.Expert opinion: In the development of new clinical diagnostics tests, MS-based metabolomics has potential as both a preliminary discovery base for routine testing and a multi-test prototype, which is hoped to be introduced into clinical practice in the near future. A laboratory-developed test (LDT) is one possible way that multi-testing could be developed.
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Affiliation(s)
- Oxana P Trifonova
- Analytical Branch, Laboratory of Mass Spectrometry-based Metabolomic Diagnostic, Institute of Biomedical Chemistry, Moscow, Russia
| | - Dmitri L Maslov
- Analytical Branch, Laboratory of Mass Spectrometry-based Metabolomic Diagnostic, Institute of Biomedical Chemistry, Moscow, Russia
| | - Elena E Balashova
- Analytical Branch, Laboratory of Mass Spectrometry-based Metabolomic Diagnostic, Institute of Biomedical Chemistry, Moscow, Russia
| | - Petr G Lokhov
- Analytical Branch, Laboratory of Mass Spectrometry-based Metabolomic Diagnostic, Institute of Biomedical Chemistry, Moscow, Russia
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12
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Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a major analytical method used in the growing field of metabolomics. Although NMR is relatively less sensitive than mass spectrometry, this analytical platform has numerous characteristics including its high reproducibility and quantitative abilities, its nonselective and noninvasive nature, and the ability to identify unknown metabolites in complex mixtures and trace the downstream products of isotope labeled substrates ex vivo, in vivo, or in vitro. Metabolomic analysis of highly complex biological mixtures has benefitted from the advances in both NMR data acquisition and analysis methods. Although metabolomics applications span a wide range of disciplines, a majority has focused on understanding, preventing, diagnosing, and managing human diseases. This chapter describes NMR-based methods relevant to the rapidly expanding metabolomics field.
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13
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Gauld C, Dumas G, Darrason M, Salles N, Desvergnes P, Philip P, Micoulaud-Franchi JA. Médecine du sommeil personnalisée et syndrome d’apnées hypopnées obstructives du sommeil : entre précision et stratification, une proposition de clarification. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.msom.2020.08.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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14
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He H, Shi X, Lawrence A, Hrovat J, Turner C, Cui JY, Gu H. 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) induces wide metabolic changes including attenuated mitochondrial function and enhanced glycolysis in PC12 cells. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2020; 201:110849. [PMID: 32559690 DOI: 10.1016/j.ecoenv.2020.110849] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 05/25/2020] [Accepted: 06/01/2020] [Indexed: 06/11/2023]
Abstract
Polybrominated diphenyl ethers (PBDEs) are extensively used as brominated flame retardants in various factory products. As environmental pollutants, the adverse effects of PBDEs on human health have been receiving considerable attention. However, the precise fundamental mechanisms of toxicity induced by PBDEs are still not fully understood. In this study, the mechanism of cytotoxicity induced by 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) was investigated by combining Seahorse XFp analysis and mass spectrometry-based metabolomics and flux approaches in PC12 cells, one of the most widely used neuron-like cell lines for investigating cytotoxic effects. The Seahorse results suggest that BDE-47 significantly attenuated mitochondrial respiration and enhanced glycolysis in PC12 cells. Additionally, metabolomics results revealed the reduction of TCA metabolites such as citrate, succinate, aconitate, malate, fumarate, and glutamate after BDE-47 exposure. Metabolic flux analysis showed that BDE-47 exposure reduced the oxidative metabolic capacity of mitochondria in PC12 cells. Furthermore, various altered metabolites were found in multiple metabolic pathways, especially in glycine-serine-threonine metabolism and glutathione metabolism. A total of 17 metabolic features were determined in order to distinguish potentially disturbed metabolite markers of BDE-47 exposure. Our findings provide possible biomarkers of cytotoxic effects induced by BDE-47 exposure, and elicit a deeper understanding of the intramolecular mechanisms that could be used in further studies to validate the potential neurotoxicity of PBDEs in vivo. Based on our results, therapeutic approaches targeting mitochondrial function and the glycolysis pathway may be a promising direction against PBDE exposure.
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Affiliation(s)
- Hailang He
- Department of Respiratory Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, 210029, PR China; Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, AZ, 85259, USA
| | - Xiaojian Shi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, AZ, 85259, USA
| | - Alex Lawrence
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, AZ, 85259, USA
| | - Jonathan Hrovat
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, AZ, 85259, USA
| | - Cassidy Turner
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, AZ, 85259, USA
| | - Julia Yue Cui
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, 98105, USA.
| | - Haiwei Gu
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, AZ, 85259, USA.
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15
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Comte B, Baumbach J, Benis A, Basílio J, Debeljak N, Flobak Å, Franken C, Harel N, He F, Kuiper M, Méndez Pérez JA, Pujos-Guillot E, Režen T, Rozman D, Schmid JA, Scerri J, Tieri P, Van Steen K, Vasudevan S, Watterson S, Schmidt HH. Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine. NETWORK AND SYSTEMS MEDICINE 2020; 3:67-90. [PMID: 32954378 PMCID: PMC7500076 DOI: 10.1089/nsm.2020.0004] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2020] [Indexed: 12/14/2022] Open
Abstract
Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.
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Affiliation(s)
- Blandine Comte
- Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Université Clermont Auvergne, INRAE, UNH, Clermont-Ferrand, France
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan (WZW), Technical University of Munich (TUM), Freising-Weihenstephan, Germany
| | | | - José Basílio
- Institute of Vascular Biology and Thrombosis Research, Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Nataša Debeljak
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- The Cancer Clinic, St. Olav's University Hospital, Trondheim, Norway
| | - Christian Franken
- Digital Health Systems, Einsingen, Germany
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | | | - Feng He
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg
- Institute of Medical Microbiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Martin Kuiper
- Department of Biology, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Juan Albino Méndez Pérez
- Department of Computer Science and Systems Engineering, Universidad de La Laguna, Tenerife, Spain
| | - Estelle Pujos-Guillot
- Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Université Clermont Auvergne, INRAE, UNH, Clermont-Ferrand, France
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Johannes A. Schmid
- Institute of Vascular Biology and Thrombosis Research, Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Jeanesse Scerri
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Sona Vasudevan
- Georgetown University Medical Centre, Washington, District of Columbia, USA
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, MeHNS, Maastricht University, The Netherlands
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16
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León-Cachón RBR, Bamford AD, Meester I, Barrera-Saldaña HA, Gómez-Silva M, Bustos MFG. The atorvastatin metabolic phenotype shift is influenced by interaction of drug-transporter polymorphisms in Mexican population: results of a randomized trial. Sci Rep 2020; 10:8900. [PMID: 32483134 PMCID: PMC7264171 DOI: 10.1038/s41598-020-65843-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 05/08/2020] [Indexed: 12/18/2022] Open
Abstract
Atorvastatin (ATV) is a blood cholesterol-lowering drug used to prevent cardiovascular events, the leading cause of death worldwide. As pharmacokinetics, metabolism and response vary among individuals, we wanted to determine the most reliable metabolic ATV phenotypes and identify novel and preponderant genetic markers that affect ATV plasma levels. A controlled, randomized, crossover, single-blind, three-treatment, three-period, and six-sequence clinical study of ATV (single 80-mg oral dose) was conducted among 60 healthy Mexican men. ATV plasma levels were measured using high-performance liquid chromatography mass spectrometry. Genotyping was performed by real-time PCR with TaqMan probes. Four ATV metabolizer phenotypes were found: slow, intermediate, normal and fast. Six gene polymorphisms, SLCO1B1-rs4149056, ABCB1-rs1045642, CYP2D6-rs1135840, CYP2B6-rs3745274, NAT2-rs1208, and COMT- rs4680, had a significant effect on ATV pharmacokinetics (P < 0.05). The polymorphisms in SLCO1B1 and ABCB1 seemed to have a greater effect and were especially important for the shift from an intermediate to a normal metabolizer. This is the first study that demonstrates how the interaction of genetic variants affect metabolic phenotyping and improves understanding of how SLCO1B1 and ABCB1 variants that affect statin metabolism may partially explain the variability in drug response. Notwithstanding, the influence of other genetic and non-genetic factors is not ruled out.
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Affiliation(s)
- Rafael B R León-Cachón
- Center of Molecular Diagnostics and Personalized Medicine, Department of Basic Sciences, Division of Health Sciences, University of Monterrey, San Pedro Garza Garcia, Nuevo Leon, Mexico.
| | - Aileen-Diane Bamford
- Center of Molecular Diagnostics and Personalized Medicine, Department of Basic Sciences, Division of Health Sciences, University of Monterrey, San Pedro Garza Garcia, Nuevo Leon, Mexico
| | - Irene Meester
- Center of Molecular Diagnostics and Personalized Medicine, Department of Basic Sciences, Division of Health Sciences, University of Monterrey, San Pedro Garza Garcia, Nuevo Leon, Mexico
| | | | - Magdalena Gómez-Silva
- Forensic Medicine Service, School of Medicine, Autonomous University of Nuevo Leon, Monterrey, Nuevo Leon, Mexico.,Analytical Department of the Research Institute for Clinical and Experimental Pharmacology, Ipharma S.A., Monterrey, Nuevo Leon, Mexico
| | - María F García Bustos
- Institute of Experimental Pathology (CONICET), Faculty of Health Sciences, National University of Salta, Salta, Argentina.,University School in Health Sciences, Catholic University of Salta, Salta, Argentina
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17
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Distinguishing NASH Histological Severity Using a Multiplatform Metabolomics Approach. Metabolites 2020; 10:metabo10040168. [PMID: 32344559 PMCID: PMC7240949 DOI: 10.3390/metabo10040168] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 04/21/2020] [Accepted: 04/21/2020] [Indexed: 12/31/2022] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD.
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18
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Botticelli A, Vernocchi P, Marini F, Quagliariello A, Cerbelli B, Reddel S, Del Chierico F, Di Pietro F, Giusti R, Tomassini A, Giampaoli O, Miccheli A, Zizzari IG, Nuti M, Putignani L, Marchetti P. Gut metabolomics profiling of non-small cell lung cancer (NSCLC) patients under immunotherapy treatment. J Transl Med 2020; 18:49. [PMID: 32014010 PMCID: PMC6998840 DOI: 10.1186/s12967-020-02231-0] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/16/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Despite the efficacy of immune checkpoint inhibitors (ICIs) only the 20-30% of treated patients present long term benefits. The metabolic changes occurring in the gut microbiota metabolome are herein proposed as a factor potentially influencing the response to immunotherapy. METHODS The metabolomic profiling of gut microbiota was characterized in 11 patients affected by non-small cell lung cancer (NSCLC) treated with nivolumab in second-line treatment with anti-PD-1 nivolumab. The metabolomics analyses were performed by GC-MS/SPME and 1H-NMR in order to detect volatile and non-volatile metabolites. Metabolomic data were processed by statistical profiling and chemometric analyses. RESULTS Four out of 11 patients (36%) presented early progression, while the remaining 7 out of 11 (64%) presented disease progression after 12 months. 2-Pentanone (ketone) and tridecane (alkane) were significantly associated with early progression, and on the contrary short chain fatty acids (SCFAs) (i.e., propionate, butyrate), lysine and nicotinic acid were significantly associated with long-term beneficial effects. CONCLUSIONS Our preliminary data suggest a significant role of gut microbiota metabolic pathways in affecting response to immunotherapy. The metabolic approach could be a promising strategy to contribute to the personalized management of cancer patients by the identification of microbiota-linked "indicators" of early progressor and long responder patients.
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Affiliation(s)
- Andrea Botticelli
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy.,AOU Policlinico Umberto I, Rome, Italy
| | - Pamela Vernocchi
- Unit of Human Microbiome, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Federico Marini
- Department of Chemistry, Sapienza University of Rome, Rome, Italy.,NMR-based Metabolomics Laboratory, Sapienza University of Rome, Rome, Italy
| | | | - Bruna Cerbelli
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy
| | - Sofia Reddel
- Unit of Human Microbiome, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | | | | | | | - Alberta Tomassini
- Department of Chemistry, Sapienza University of Rome, Rome, Italy.,NMR-based Metabolomics Laboratory, Sapienza University of Rome, Rome, Italy
| | | | - Alfredo Miccheli
- NMR-based Metabolomics Laboratory, Sapienza University of Rome, Rome, Italy.,Department of Enviromental Biology, University of Rome, Rome, Italy
| | | | - Marianna Nuti
- Department of Experimental Medicine, University Sapienza, Rome, Italy
| | - Lorenza Putignani
- Unit of Parasitology and Unit of Human Microbiome, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
| | - Paolo Marchetti
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy.,AOU Policlinico Umberto I, Rome, Italy.,AOU Sant'Andrea Hospital, Rome, Italy
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19
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Metabolomic and Lipidomic Signatures of Metabolic Syndrome and its Physiological Components in Adults: A Systematic Review. Sci Rep 2020; 10:669. [PMID: 31959772 PMCID: PMC6971076 DOI: 10.1038/s41598-019-56909-7] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 12/19/2019] [Indexed: 12/20/2022] Open
Abstract
The aim of this work was to conduct a systematic review of human studies on metabolite/lipid biomarkers of metabolic syndrome (MetS) and its components, and provide recommendations for future studies. The search was performed in MEDLINE, EMBASE, EMB Review, CINHAL Complete, PubMed, and on grey literature, for population studies identifying MetS biomarkers from metabolomics/lipidomics. Extracted data included population, design, number of subjects, sex/gender, clinical characteristics and main outcome. Data were collected regarding biological samples, analytical methods, and statistics. Metabolites were compiled by biochemical families including listings of their significant modulations. Finally, results from the different studies were compared. The search yielded 31 eligible studies (2005–2019). A first category of articles identified prevalent and incident MetS biomarkers using mainly targeted metabolomics. Even though the population characteristics were quite homogeneous, results were difficult to compare in terms of modulated metabolites because of the lack of methodological standardization. A second category, focusing on MetS components, allowed comparing more than 300 metabolites, mainly associated with the glycemic component. Finally, this review included also publications studying type 2 diabetes as a whole set of metabolic risks, raising the interest of reporting metabolomics/lipidomics signatures to reflect the metabolic phenotypic spectrum in systems approaches.
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20
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Gómez-Silva M, Piñeyro-Garza E, Vargas-Zapata R, Gamino-Peña ME, León-García A, de León MB, Llerena A, León-Cachón RBR. Pharmacogenetics of amfepramone in healthy Mexican subjects reveals potential markers for tailoring pharmacotherapy of obesity: results of a randomised trial. Sci Rep 2019; 9:17833. [PMID: 31780765 PMCID: PMC6882847 DOI: 10.1038/s41598-019-54436-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 11/12/2019] [Indexed: 12/17/2022] Open
Abstract
Amfepramone (AFP) is an appetite-suppressant drug used in the treatment of obesity. Nonetheless, studies on interindividual pharmacokinetic variability and its association with genetic variants are limited. We employed a pharmacokinetic and pharmacogenetic approach to determine possible metabolic phenotypes of AFP and identify genetic markers that could affect the pharmacokinetic variability in a Mexican population. A controlled, randomized, crossover, single-blind, two-treatment, two-period, and two sequence clinical study of AFP (a single 75 mg dose) was conducted in 36 healthy Mexican volunteers who fulfilled the study requirements. Amfepramone plasma levels were measured using high-performance liquid chromatography mass spectrometry. Genotyping was performed using real-time PCR with TaqMan probes. Four AFP metabolizer phenotypes were found in our population: slow, normal, intermediate, and fast. Additionally, two gene polymorphisms, ABCB1-rs1045642 and CYP3A4-rs2242480, had a significant effect on AFP pharmacokinetics (P < 0.05) and were the predictor factors in a log-linear regression model. The ABCB1 and CYP3A4 gene polymorphisms were associated with a fast metabolizer phenotype. These results suggest that metabolism of AFP in the Mexican population is variable. In addition, the genetic variants ABCB1-rs1045642 and CYP3A4-rs2242480 may partially explain the AFP pharmacokinetic variability.
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Affiliation(s)
- Magdalena Gómez-Silva
- Forensic Medicine Service, School of Medicine, Autonomous University of Nuevo Leon, Monterrey, Nuevo Leon, Mexico.,Analytical Department of the Research Institute for Clinical and Experimental Pharmacology, Ipharma S.A, Monterrey, Nuevo Leon, Mexico
| | - Everardo Piñeyro-Garza
- Clinical Department of the Research Institute for Clinical and Experimental Pharmacology, Ipharma S.A, Monterrey, Nuevo Leon, Mexico
| | - Rigoberto Vargas-Zapata
- Quality Assurance Department of the Research Institute for Clinical and Experimental Pharmacology, Ipharma S.A, Monterrey, Nuevo Leon, Mexico
| | - María Elena Gamino-Peña
- Statistical Department of the Research Institute for Clinical and Experimental Pharmacology, Ipharma S.A, Monterrey, Nuevo Leon, Mexico
| | | | - Mario Bermúdez de León
- Department of Molecular Biology, Center for Biomedical Research of the Northeast, Mexican Institute of Social Security, Monterrey, Nuevo Leon, Mexico
| | - Adrián Llerena
- Clinical Research Center of Health Area, Hospital and Medical School of Extremadura University, Badajoz, Spain
| | - Rafael B R León-Cachón
- Center of Molecular Diagnostics and Personalized Medicine, Department of Basic Sciences, Division of Health Sciences, University of Monterrey, San Pedro Garza Garcia, Nuevo Leon, Mexico.
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21
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Azad RK, Shulaev V. Metabolomics technology and bioinformatics for precision medicine. Brief Bioinform 2019; 20:1957-1971. [PMID: 29304189 PMCID: PMC6954408 DOI: 10.1093/bib/bbx170] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 11/29/2017] [Indexed: 12/14/2022] Open
Abstract
Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.
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Affiliation(s)
| | - Vladimir Shulaev
- Corresponding author: Vladimir Shulaev, Department of Biological Sciences, BioDiscovery Institute, University of North Texas, Denton, TX 76210, USA. Tel.: 940-369-5368; Fax: 940-565-3821; E-mail:
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22
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Picca A, Coelho-Junior HJ, Cesari M, Marini F, Miccheli A, Gervasoni J, Bossola M, Landi F, Bernabei R, Marzetti E, Calvani R. The metabolomics side of frailty: Toward personalized medicine for the aged. Exp Gerontol 2019; 126:110692. [PMID: 31421185 DOI: 10.1016/j.exger.2019.110692] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 07/24/2019] [Accepted: 08/13/2019] [Indexed: 12/12/2022]
Abstract
Frailty encompasses several domains (i.e., metabolic, physical, cognitive). The multisystem derangements underlying frailty pathophysiology, its phenotypic heterogeneity, and the fluctuations of individuals across severity states have hampered a comprehensive appraisal of the condition. Circulating biomarkers emerged as an alleged tool for capturing this complexity and, as proxies for organismal metabolic changes, may hold the advantages of: 1) supporting diagnosis, 2) tracking the progression, 3) assisting healthcare professionals in clinical and therapeutic decision-making, and 4) verifying the efficacy of an intervention before measurable clinical manifestations occur. Among available analytical tools, metabolomics are able to identify and quantify the (ideally) whole repertoire of small molecules in biological matrices (i.e., cells, tissues, and biological fluids). Results of metabolomics analysis may define the final output of genome-environment interactions at the individual level. This entire collection of metabolites is called "metabolome" and is highly dynamic. Here, we discuss how monitoring the dynamics of metabolic profiles may provide a read-out of the environmental and clinical disturbances affecting cell homeostasis in frailty-associated conditions.
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Affiliation(s)
- Anna Picca
- Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, 00168 Rome, Italy; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168 Rome, Italy
| | - Hélio José Coelho-Junior
- Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, 00168 Rome, Italy; Applied Kinesiology Laboratory-LCA, School of Physical Education, University of Campinas, 13.083-851 Campinas, SP, Brazil
| | - Matteo Cesari
- Department of Clinical Sciences and Community Health, Università di Milano, 20122 Milan, Italy; Geriatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Federico Marini
- Department of Chemistry, Sapienza Università di Roma, 00168 Rome, Italy
| | - Alfredo Miccheli
- Department of Chemistry, Sapienza Università di Roma, 00168 Rome, Italy
| | - Jacopo Gervasoni
- Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, 00168 Rome, Italy; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168 Rome, Italy
| | - Maurizio Bossola
- Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, 00168 Rome, Italy; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168 Rome, Italy
| | - Francesco Landi
- Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, 00168 Rome, Italy; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168 Rome, Italy
| | - Roberto Bernabei
- Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, 00168 Rome, Italy; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168 Rome, Italy.
| | - Emanuele Marzetti
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168 Rome, Italy.
| | - Riccardo Calvani
- Università Cattolica del Sacro Cuore, Institute of Internal Medicine and Geriatrics, 00168 Rome, Italy; Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168 Rome, Italy
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23
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Lécuyer L, Dalle C, Lyan B, Demidem A, Rossary A, Vasson MP, Petera M, Lagree M, Ferreira T, Centeno D, Galan P, Hercberg S, Deschasaux M, Partula V, Srour B, Latino-Martel P, Kesse-Guyot E, Druesne-Pecollo N, Durand S, Pujos-Guillot E, Touvier M. Plasma Metabolomic Signatures Associated with Long-term Breast Cancer Risk in the SU.VI.MAX Prospective Cohort. Cancer Epidemiol Biomarkers Prev 2019; 28:1300-1307. [PMID: 31164347 DOI: 10.1158/1055-9965.epi-19-0154] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/12/2019] [Accepted: 05/28/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Breast cancer is a major cause of death in occidental women. The role of metabolism in breast cancer etiology remains unclear. Metabolomics may help to elucidate novel biological pathways and identify new biomarkers to predict breast cancer long before symptoms appear. The aim of this study was to investigate whether untargeted metabolomic signatures from blood draws of healthy women could contribute to better understand and predict the long-term risk of developing breast cancer. METHODS A nested case-control study was conducted within the SU.VI.MAX prospective cohort (13 years of follow-up) to analyze baseline plasma samples of 211 incident breast cancer cases and 211 matched controls by LC/MS. Multivariable conditional logistic regression models were computed. RESULTS A total of 3,565 ions were detected and 1,221 were retained for statistical analysis. A total of 73 ions were associated with breast cancer risk (P < 0.01; FDR ≤ 0.2). Notably, we observed that a lower plasma level of O-succinyl-homoserine (OR = 0.70, 95%CI = [0.55-0.89]) and higher plasma levels of valine/norvaline [1.45 (1.15-1.83)], glutamine/isoglutamine [1.33 (1.07-1.66)], 5-aminovaleric acid [1.46 (1.14-1.87)], phenylalanine [1.43 (1.14-1.78)], tryptophan [1.40 (1.10-1.79)], γ-glutamyl-threonine [1.39 (1.09-1.77)], ATBC [1.41 (1.10-1.79)], and pregnene-triol sulfate [1.38 (1.08-1.77)] were associated with an increased risk of developing breast cancer during follow-up.Conclusion: Several prediagnostic plasmatic metabolites were associated with long-term breast cancer risk and suggested a role of microbiota metabolism and environmental exposure. IMPACT After confirmation in other independent cohort studies, these results could help to identify healthy women at higher risk of developing breast cancer in the subsequent decade and to propose a better understanding of the complex mechanisms involved in its etiology.
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Affiliation(s)
- Lucie Lécuyer
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France.
| | - Céline Dalle
- Clermont Auvergne University, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Bernard Lyan
- Clermont Auvergne University, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Aicha Demidem
- Clermont Auvergne University, INRA, UMR 1019, Human Nutrition Unit (UNH), CRNH Auvergne, Cellular Micro-Environment, Immunomodulation and Nutrition (ECREIN), Clermont-Ferrand, France
| | - Adrien Rossary
- Clermont Auvergne University, INRA, UMR 1019, Human Nutrition Unit (UNH), CRNH Auvergne, Cellular Micro-Environment, Immunomodulation and Nutrition (ECREIN), Clermont-Ferrand, France
| | - Marie-Paule Vasson
- Clermont Auvergne University, INRA, UMR 1019, Human Nutrition Unit (UNH), CRNH Auvergne, Cellular Micro-Environment, Immunomodulation and Nutrition (ECREIN), Clermont-Ferrand, France.,Anticancer Center Jean-Perrin, CHU Clermont-Ferrand, France
| | - Mélanie Petera
- Clermont Auvergne University, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Marie Lagree
- Clermont Auvergne University, Institut de Chimie de Clermont-Ferrand, Plateforme d'Exploration du Métabolisme, MetaboHUB-Clermont, BP 80026, Aubière, France
| | - Thomas Ferreira
- Clermont Auvergne University, INRA, UMR 1019, Human Nutrition Unit (UNH), CRNH Auvergne, Cellular Micro-Environment, Immunomodulation and Nutrition (ECREIN), Clermont-Ferrand, France
| | - Delphine Centeno
- Clermont Auvergne University, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Pilar Galan
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France
| | - Serge Hercberg
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France.,Public Health Department, Avicenne Hospital, Bobigny, France
| | - Mélanie Deschasaux
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France
| | - Valentin Partula
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France
| | - Bernard Srour
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France
| | - Paule Latino-Martel
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France
| | - Emmanuelle Kesse-Guyot
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France
| | - Nathalie Druesne-Pecollo
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France
| | - Stéphanie Durand
- Clermont Auvergne University, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Estelle Pujos-Guillot
- Clermont Auvergne University, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Mathilde Touvier
- Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), French National Institute of Health and Medical Research (Inserm) U1153, French National Institute for Agricultural Research (Inra) U1125, French National Conservatory of Arts and Crafts (Cnam), Paris 13 University, Nutritional Epidemiology Research Team (EREN), Bobigny, France
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Barba I, Andrés M, Garcia-Dorado D. Metabolomics and Heart Diseases: From Basic to Clinical Approach. Curr Med Chem 2019; 26:46-59. [PMID: 28990507 DOI: 10.2174/0929867324666171006151408] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 03/15/2017] [Accepted: 04/03/2017] [Indexed: 12/14/2022]
Abstract
BACKGROUND The field of metabolomics has been steadily increasing in size for the last 15 years. Advances in analytical and statistical methods have allowed metabolomics to flourish in various areas of medicine. Cardiovascular diseases are some of the main research targets in metabolomics, due to their social and medical relevance, and also to the important role metabolic alterations play in their pathogenesis and evolution. Metabolomics has been applied to the full spectrum of cardiovascular diseases: from patient risk stratification to myocardial infarction and heart failure. However - despite the many proof-ofconcept studies describing the applicability of metabolomics in the diagnosis, prognosis and treatment evaluation in cardiovascular diseases - it is not yet used in routine clinical practice. Recently, large phenome centers have been established in clinical environments, and it is expected that they will provide definitive proof of the applicability of metabolomics in clinical practice. But there is also room for small and medium size centers to work on uncommon pathologies or to resolve specific but relevant clinical questions. OBJECTIVES In this review, we will introduce metabolomics, cover the metabolomic work done so far in the area of cardiovascular diseases. CONCLUSION The cardiovascular field has been at the forefront of metabolomics application and it should lead the transfer to the clinic in the not so distant future.
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Affiliation(s)
- Ignasi Barba
- Cardiovascular Diseases Research Group, Department of Cardiology, Vall d'Hebron University Hospital and Research Institute, Universitat Autonoma de Barcelona, Barcelona, Spain.,Centro de Investigacion Biomedica en Red sobre Enfermedades Cardiovasculares (CIBER-CV), Madrid, Spain
| | - Mireia Andrés
- Cardiovascular Diseases Research Group, Department of Cardiology, Vall d'Hebron University Hospital and Research Institute, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - David Garcia-Dorado
- Cardiovascular Diseases Research Group, Department of Cardiology, Vall d'Hebron University Hospital and Research Institute, Universitat Autonoma de Barcelona, Barcelona, Spain.,Centro de Investigacion Biomedica en Red sobre Enfermedades Cardiovasculares (CIBER-CV), Madrid, Spain
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25
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Fernandez O, Urrutia M, Berton T, Bernillon S, Deborde C, Jacob D, Maucourt M, Maury P, Duruflé H, Gibon Y, Langlade NB, Moing A. Metabolomic characterization of sunflower leaf allows discriminating genotype groups or stress levels with a minimal set of metabolic markers. Metabolomics 2019; 15:56. [PMID: 30929085 PMCID: PMC6441456 DOI: 10.1007/s11306-019-1515-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 03/18/2019] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Plant and crop metabolomic analyses may be used to study metabolism across genetic and environmental diversity. Complementary analytical strategies are useful for investigating metabolic changes and searching for biomarkers of response or performance. METHODS AND OBJECTIVES The experimental material consisted in eight sunflower lines with two line status, four restorers (R, used as males) and four maintainers (B, corresponding to females) routinely used for sunflower hybrid varietal production, respectively to complement or maintain the cytoplasmic male sterility PET1. These lines were either irrigated at full soil capacity (WW) or submitted to drought stress (DS). Our aim was to combine targeted and non-targeted metabolomics to characterize sunflower leaf composition in order to investigate the effect of line status genotypes and environmental conditions and to find the best and smallest set of biomarkers for line status and stress response using a custom-made process of variables selection. RESULTS Five hundred and eighty-eight metabolic variables were measured by using complementary analytical methods such as 1H-NMR, MS-based profiles and targeted analyses of major metabolites. Based on statistical analyses, a limited number of markers were able to separate WW and DS samples in a more discriminant manner than previously published physiological data. Another metabolic marker set was able to discriminate line status. CONCLUSION This study underlines the potential of metabolic markers for discriminating genotype groups and environmental conditions. Their potential use for prediction is discussed.
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Affiliation(s)
- Olivier Fernandez
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Present Address: Laboratoire RIBP, Université de Reims Champagne Ardenne, Moulin de la Housse Chemin des Rouliers, 51100 Reims, France
| | - Maria Urrutia
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- UMR AgroImpact, INRA, Estrées-Mons, 80203 Péronne, France
- Present Address: Enza Zaden Centro de Investigacion S.L., Santa Maria del Aguila, 04710 Almeria, Spain
| | - Thierry Berton
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Present Address: Centre for CardioVascular and Nutrition, UMR INRA-INSERM, Aix-Marseille Univ, INSERM, 13005 Marseilles, France
| | - Stéphane Bernillon
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Catherine Deborde
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Daniel Jacob
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Mickaël Maucourt
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
- Present Address: Enza Zaden Centro de Investigacion S.L., Santa Maria del Aguila, 04710 Almeria, Spain
| | - Pierre Maury
- UMR LIPM, INRA, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Harold Duruflé
- UMR LIPM, INRA, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Yves Gibon
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Nicolas B. Langlade
- UMR LIPM, INRA, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Annick Moing
- UMR1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
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26
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Rozendaal YJW, Wang Y, Hilbers PAJ, van Riel NAW. Computational modelling of energy balance in individuals with Metabolic Syndrome. BMC SYSTEMS BIOLOGY 2019; 13:24. [PMID: 30808366 PMCID: PMC6390597 DOI: 10.1186/s12918-019-0705-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 02/14/2019] [Indexed: 02/07/2023]
Abstract
Background A positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expenditure and energy balance in a large set of individual model realizations. Results We developed and applied a strategy to select specific individual models for a detailed analysis of heterogeneity in energy metabolism. Models were stratified based on energy expenditure. A substantial surplus of energy was found to be present during MetS development, which explains the weight gain during MetS development. In the majority of the models, energy was mainly expended in the peripheral tissues, but also distinctly different subgroups were identified. In silico perturbation of the system to induce increased peripheral energy expenditure implied changes in lipid metabolism, but not in carbohydrate metabolism. In silico analysis provided predictions for which individual models increase of peripheral energy expenditure would be an effective treatment. Conclusion The computational analysis confirmed that the energy imbalance plays an important role in the development of obesity. Furthermore, the model is capable to predict whether an increase in peripheral energy expenditure – for instance by cold exposure to activate brown adipose tissue (BAT) – could resolve MetS symptoms. Electronic supplementary material The online version of this article (10.1186/s12918-019-0705-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yvonne J W Rozendaal
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Yanan Wang
- Department of Pediatrics, Section Molecular Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department of Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A J Hilbers
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. .,Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands.
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Fei Q, Wang D, Jasbi P, Zhang P, Nagana Gowda GA, Raftery D, Gu H. Combining NMR and MS with Chemical Derivatization for Absolute Quantification with Reduced Matrix Effects. Anal Chem 2019; 91:4055-4062. [DOI: 10.1021/acs.analchem.8b05611] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Qiang Fei
- College of Chemistry, Jilin University, Changchun 130021, P. R. China
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, United States
| | - Dongfang Wang
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, United States
- Chongqing Blood Center, Chongqing 400015, P. R. China
| | - Paniz Jasbi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
| | - Ping Zhang
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, United States
- College of Plant Protection, Southwest University, Chongqing 400715, P. R. China
| | - G. A. Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, United States
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, United States
- Department of Chemistry, University of Washington, Seattle, Washington 98109, United States
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
| | - Haiwei Gu
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, Arizona 85259, United States
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Fernando T, Sawala A, Bailey AP, Gould AP, Driscoll PC. An Improved Method for Measuring Absolute Metabolite Concentrations in Small Biofluid or Tissue Samples. J Proteome Res 2019; 18:1503-1512. [PMID: 30757904 PMCID: PMC6456871 DOI: 10.1021/acs.jproteome.8b00773] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
The
measurement of absolute metabolite concentrations in small
samples remains a significant analytical challenge. This is particularly
the case when the sample volume is only a few microliters or less
and cannot be determined accurately via direct measurement. We previously
developed volume determination with two standards (VDTS) as a method
to address this challenge for biofluids. As a proof-of-principle,
we applied VDTS to NMR spectra of polar metabolites in the hemolymph
(blood) of the tiny yet powerful genetic model Drosophila
melanogaster. This showed that VDTS calculation of absolute
metabolite concentrations in fed versus starved Drosophila larvae is more accurate than methods utilizing normalization to
total spectral signal. Here, we introduce paired VDTS (pVDTS), an
improved VDTS method for biofluids and solid tissues that implements
the statistical power of paired control and experimental replicates.
pVDTS utilizes new equations that also include a correction for dilution
errors introduced by the variable surface wetness of solid samples.
We then show that metabolite concentrations in Drosophila larvae are more precisely determined and logically consistent using
pVDTS than using the original VDTS method. The refined pVDTS workflow
described in this study is applicable to a wide range of different
tissues and biofluids.
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Affiliation(s)
- Tharindu Fernando
- Physiology and Metabolism Laboratory , The Francis Crick Institute , 1 Midland Road , London NW1 1AT , U.K
| | - Annick Sawala
- Physiology and Metabolism Laboratory , The Francis Crick Institute , 1 Midland Road , London NW1 1AT , U.K
| | - Andrew P Bailey
- Physiology and Metabolism Laboratory , The Francis Crick Institute , 1 Midland Road , London NW1 1AT , U.K
| | - Alex P Gould
- Physiology and Metabolism Laboratory , The Francis Crick Institute , 1 Midland Road , London NW1 1AT , U.K
| | - Paul C Driscoll
- Metabolomics Science Technology Platform , The Francis Crick Institute , 1 Midland Road , London NW1 1AT , U.K
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29
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Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Emami Khonsari P, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, Steinbeck C. PhenoMeNal: processing and analysis of metabolomics data in the cloud. Gigascience 2019; 8:giy149. [PMID: 30535405 PMCID: PMC6377398 DOI: 10.1093/gigascience/giy149] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 10/19/2018] [Accepted: 11/20/2018] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.
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Affiliation(s)
- Kristian Peters
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - James Bradbury
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marco Capuccini
- Division of Scientific Computing, Department of Information Technology, Uppsala University, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Timothy M D Ebbels
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Robert Glen
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB21EW, United Kingdom
| | - Alejandra Gonzalez-Beltran
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Ulrich L Günther
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Evangelos Handakas
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Thomas Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Stephanie Herman
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | | | - Massimiliano Izzo
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Daniel Jacob
- INRA, University of Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 33140 Villenave d'Ornon, France
| | - David Johnson
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
- Department of Informatics and Media, Uppsala University, Box 513, 751 20 Uppsala, Sweden
| | - Fabien Jourdan
- INRA - French National Institute for Agricultural Research, UMR1331, Toxalim, Research Centre in Food Toxicology, Toulouse, France
| | - Namrata Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, W2 1PG, London, United Kingdom
| | - Bita Khalili
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Payam Emami Khonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Anders Larsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Christian Ludwig
- Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
| | - Jon Ander Novella
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Jake T M Pearce
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Alina Peluso
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | | | | | - Michelle A C Reed
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Pierrick Roger
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Antonio Rosato
- Magnetic Resonance Center (CERM) and Department of Chemistry, University of Florence and CIRMMP, 50019 Sesto Fiorentino, Florence, Italy
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Noureddin Sadawi
- Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University, London, UK
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Vitaly Selivanov
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Daniel Schober
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Mattia Tomasoni
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Merlijn van Rijswijk
- Netherlands Metabolomics Center, Leiden, 2333 CC, Netherlands
- ELIXIR-NL, Dutch Techcentre for Life Sciences, Utrecht, 3503 RM, Netherlands
| | - Michael van Vliet
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Ralf J M Weber
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | | | - Christoph Steinbeck
- Cheminformatics and Computational Metabolomics, Institute for Analytical Chemistry, Lessingstr. 8, 07743 Jena, Germany
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30
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Pujos-Guillot E, Pétéra M, Jacquemin J, Centeno D, Lyan B, Montoliu I, Madej D, Pietruszka B, Fabbri C, Santoro A, Brzozowska A, Franceschi C, Comte B. Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics. Front Physiol 2019; 9:1903. [PMID: 30733683 PMCID: PMC6353829 DOI: 10.3389/fphys.2018.01903] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 12/18/2018] [Indexed: 01/14/2023] Open
Abstract
Aging is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. Pre-frailty is still not well understood but it has been associated with global imbalance in several physiological systems, including inflammation, and in nutrition. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is essential to move toward more personalized care. The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics, in order to identify specific biomarkers, and study their stability over time. The approach was based on the NU-AGE project (clinicaltrials.gov, NCT01754012) that regrouped 1,250 free-living elderly people (65–79 y.o., men and women), free of major diseases, recruited within five European centers. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al. (2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centers were selected for untargeted serum metabolomics at T0 (baseline) and T1 (follow-up). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate models. Metabolomics enabled to discriminate sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility. The best resulting models included four different metabolites for each gender. They showed very good prediction capacity with AUCs of 0.93 (95% CI = 0.87–1) and 0.94 (95% CI = 0.87–1) for men and women, respectively. Additionally, early and/or predictive markers of pre-frailty were identified for both genders and the gender specific models showed also good performance (three metabolites; AUC = 0.82; 95% CI = 0.72–0.93) for men and very good for women (three metabolites; AUC = 0.92; 95% CI = 0.86–0.99). These results open the door, through multivariate strategies, to a possibility of monitoring the disease progression over time at a very early stage.
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Affiliation(s)
- Estelle Pujos-Guillot
- Université Clermont Auvergne, Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Centre Auvergne Rhône Alpes, Clermont-Ferrand, France.,Université Clermont Auvergne, Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Mélanie Pétéra
- Université Clermont Auvergne, Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Jérémie Jacquemin
- Université Clermont Auvergne, Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Delphine Centeno
- Université Clermont Auvergne, Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Bernard Lyan
- Université Clermont Auvergne, Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Ivan Montoliu
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Dawid Madej
- Department of Human Nutrition, Warsaw University of Life Sciences - Szkoła Główna Gospodarstwa Wiejskiego, Warsaw, Poland
| | - Barbara Pietruszka
- Department of Human Nutrition, Warsaw University of Life Sciences - Szkoła Główna Gospodarstwa Wiejskiego, Warsaw, Poland
| | - Cristina Fabbri
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Aurelia Santoro
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.,Interdepartmental Center "L. Galvani", University of Bologna, Bologna, Italy
| | - Anna Brzozowska
- Department of Human Nutrition, Warsaw University of Life Sciences - Szkoła Główna Gospodarstwa Wiejskiego, Warsaw, Poland
| | | | - Blandine Comte
- Université Clermont Auvergne, Institut National de la Recherche Agronomique, Unité de Nutrition Humaine, Centre Auvergne Rhône Alpes, Clermont-Ferrand, France
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31
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Jasbi P, Wang D, Cheng SL, Fei Q, Cui JY, Liu L, Wei Y, Raftery D, Gu H. Breast cancer detection using targeted plasma metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci 2019; 1105:26-37. [DOI: 10.1016/j.jchromb.2018.11.029] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 11/27/2018] [Accepted: 11/30/2018] [Indexed: 12/11/2022]
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32
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Alterations of eicosanoids and related mediators in patients with schizophrenia. J Psychiatr Res 2018; 102:168-178. [PMID: 29674269 DOI: 10.1016/j.jpsychires.2018.04.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 03/22/2018] [Accepted: 04/04/2018] [Indexed: 02/08/2023]
Abstract
Schizophrenia (SCZ) is a multifactorial psychiatric disorder. Currently, its molecular pathogenesis remains largely unknown, and no reliable test for diagnosis and therapy monitoring is available. Polyunsaturated fatty acids (PUFAs) and their derived eicosanoid signaling abnormalities are relevant to the pathophysiology of schizophrenia. However, comprehensive analysis of eicosanoids and related mediators for schizophrenia is very rare. In this study, we applied a targeted liquid chromatography-mass spectrometry based method to monitor 158 PUFAs, eicosanoids and related mediators from enzyme-dependent or independent pathways, in the serum samples of 109 healthy controls, and 115 schizophrenia patients at baseline and after an 8-week period of antipsychotic therapy. Twenty-three metabolites were identified to be significantly altered in SCZ patients at baseline compared to healthy controls, especially arachidonic acid (AA) derived eicosanoids. These disturbances may be related to altered immunological reactions and neurotransmitter signaling. After 8-week antipsychotic treatment, there were 22 metabolites, especially AA and linoleic acid derived eicosanoids, significantly altered in posttreatment patients. Some metabolites, such as several AA derived prostaglandins, thromboxanes, and di-hydroxy-eicosatrienoic acids were reversed toward normal levels after treatment. Based on univariate analysis and orthogonal partial least-squares discriminant analysis, anandamide, oleoylethanolamine, and AA were selected as a panel of potential biomarkers for differentiating baseline SCZ patients from controls, which showed a high sensitivity (0.907), good specificity (0.843) and excellent area under the receiver operating characteristic curve (0.940). This study provided a new perspective to understand the pathophysiological mechanism and identify potential biomarkers of SCZ.
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33
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Koshiba S, Motoike I, Saigusa D, Inoue J, Shirota M, Katoh Y, Katsuoka F, Danjoh I, Hozawa A, Kuriyama S, Minegishi N, Nagasaki M, Takai-Igarashi T, Ogishima S, Fuse N, Kure S, Tamiya G, Tanabe O, Yasuda J, Kinoshita K, Yamamoto M. Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project. Genes Cells 2018; 23:406-417. [PMID: 29701317 DOI: 10.1111/gtc.12588] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 03/22/2018] [Indexed: 01/05/2023]
Abstract
Population-based prospective cohort studies are indispensable for modern medical research as they provide important knowledge on the influences of many kinds of genetic and environmental factors on the cause of disease. Although traditional cohort studies are mainly conducted using questionnaires and physical examinations, modern cohort studies incorporate omics and genomic approaches to obtain comprehensive physical information, including genetic information. Here, we report the design and midterm results of multi-omics analysis on population-based prospective cohort studies from the Tohoku Medical Megabank (TMM) Project. We have incorporated genomic and metabolomic studies in the TMM cohort study as both metabolome and genome analyses are suitable for high-throughput analysis of large-scale cohort samples. Moreover, an association study between the metabolome and genome show that metabolites are an important intermediate phenotype connecting genetic and lifestyle factors to physical and pathologic phenotypes. We apply our metabolome and genome analyses to large-scale cohort samples in the following studies.
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Affiliation(s)
- Seizo Koshiba
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Ikuko Motoike
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Daisuke Saigusa
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Jin Inoue
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Matsuyuki Shirota
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yasutake Katoh
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Fumiki Katsuoka
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Inaho Danjoh
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Atsushi Hozawa
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Shinichi Kuriyama
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
| | - Naoko Minegishi
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Masao Nagasaki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Takako Takai-Igarashi
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Nobuo Fuse
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Shigeo Kure
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Osamu Tanabe
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Jun Yasuda
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
- Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
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Metabolomic prediction of treatment outcome in pancreatic ductal adenocarcinoma patients receiving gemcitabine. Cancer Chemother Pharmacol 2017; 81:277-289. [PMID: 29196965 DOI: 10.1007/s00280-017-3475-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2017] [Accepted: 11/03/2017] [Indexed: 02/06/2023]
Abstract
PURPOSE Resistance to gemcitabine remains a key challenge in the treatment of pancreatic ductal adenocarcinoma (PDAC), necessitating the constant search for effective strategies for a priori prediction of clinical outcome. While the existing studies focused on aberration of drug disposition genes and proteins as molecular predictors of gemcitabine treatment outcomes, the metabolic aberration associated with chemoresistance in clinical PDAC has been neglected. This exploratory study investigated the potential role of tissue metabolomics in characterizing the clinical treatment outcome of gemcitabine therapy. METHODS Surgically resected tumors from PDAC patients who underwent gemcitabine-based adjuvant chemotherapy (n = 25) were subjected to metabotyping using gas chromatography/time-of-flight mass spectrometry (GC/TOFMS). RESULTS A partial least-squares discriminant analysis (PLS-DA) model clearly distinguished patients who had favorable survival [overall survival (OS) > 24 months] from those who exhibited poorer survival (OS < 16 months) (Q 2 = 0.302). Receiver-operating characteristic analysis demonstrated the robustness of the PLS-DA model with an area under the curve of 1. PLS-DA revealed 19 marker metabolites (e.g., lactic acid, proline, and pyroglutamate) that shed insights into the chemoresistance of gemcitabine in PDAC. Particularly, tissue levels of lactic acid complemented transcript expression levels of human equilibrative nucleoside transporter 1 in distinguishing patients according to their overall survival. CONCLUSION This work established proof-of-principle for GC/TOFMS-based global metabotyping of PDAC and laid the foundation for future discovery of metabolic biomarkers predictive of gemcitabine resistance in PDAC chemotherapy.
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Würtz P, Kangas AJ, Soininen P, Lawlor DA, Davey Smith G, Ala-Korpela M. Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies. Am J Epidemiol 2017; 186:1084-1096. [PMID: 29106475 PMCID: PMC5860146 DOI: 10.1093/aje/kwx016] [Citation(s) in RCA: 315] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Accepted: 01/19/2017] [Indexed: 12/13/2022] Open
Abstract
Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.
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Affiliation(s)
- Peter Würtz
- Correspondence to Dr. Peter Würtz, Computational Medicine, Faculty of Medicine, Aapistie 5A, P.O. Box 5000, FI-90014 University of Oulu, Finland (e-mail: ); or Dr. Mika Ala-Korpela, Computational Medicine, Faculty of Medicine, Aapistie 5A, P.O. Box 5000, FI-90014 University of Oulu, Finland (e-mail: )
| | | | | | | | | | - Mika Ala-Korpela
- Correspondence to Dr. Peter Würtz, Computational Medicine, Faculty of Medicine, Aapistie 5A, P.O. Box 5000, FI-90014 University of Oulu, Finland (e-mail: ); or Dr. Mika Ala-Korpela, Computational Medicine, Faculty of Medicine, Aapistie 5A, P.O. Box 5000, FI-90014 University of Oulu, Finland (e-mail: )
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Costa Pereira J, Jarak I, Carvalho RA. Resolving NMR signals of short-chain fatty acid mixtures using unsupervised component analysis. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2017; 55:936-943. [PMID: 28480544 DOI: 10.1002/mrc.4606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 04/28/2017] [Accepted: 05/03/2017] [Indexed: 06/07/2023]
Abstract
Nuclear magnetic resonance (NMR) is a very powerful instrumental technique suited to identify and characterize organic compounds. NMR has been successfully used in the analysis of complex biological and environmental samples; however, these applications are still rather limited. In this work, we describe unsupervised component analysis as a multivariate unsupervised method suited to identify the number of relevant NMR signal contributions and to deconvolve mixed signals into signal individual sources and respective contributions. Using this approach, we were able to advance further in the field of quantification of NMR spectra, and this methodology will help in the characterization of complex biological samples. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Jorge Costa Pereira
- CQC, Department of Chemistry, University of Coimbra, P-3004 535, Coimbra, Portugal
| | - Ivana Jarak
- CICS-UBI, University of Beira Interior, 6201-506, Covilha, Portugal
- Centre for Functional Ecology, Faculty of Sciences and Technology, University of Coimbra, P-3000 456, Coimbra, Portugal
| | - Rui Albuquerque Carvalho
- Centre for Functional Ecology, Faculty of Sciences and Technology, University of Coimbra, P-3000 456, Coimbra, Portugal
- Department of Life Sciences, Faculty of Sciences and Technology, University of Coimbra, P-3000 456, Coimbra, Portugal
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Maniscalco M, Motta A. Metabolomics of exhaled breath condensate: a means for phenotyping respiratory diseases? Biomark Med 2017; 11:405-407. [PMID: 28617073 DOI: 10.2217/bmm-2017-0068] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Mauro Maniscalco
- Pulmonary Rehabilitation Unit, ICS Maugeri SPA, IRCCS, 82037 Telese Terme, Benevento, Italy
| | - Andrea Motta
- Institute of Biomolecular Chemistry, National Research Council, 80078 Pozzuoli, Naples, Italy
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Marshall DD, Powers R. Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2017; 100:1-16. [PMID: 28552170 PMCID: PMC5448308 DOI: 10.1016/j.pnmrs.2017.01.001] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Revised: 01/04/2017] [Accepted: 01/08/2017] [Indexed: 05/02/2023]
Abstract
Metabolomics is undergoing tremendous growth and is being employed to solve a diversity of biological problems from environmental issues to the identification of biomarkers for human diseases. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are the analytical tools that are routinely, but separately, used to obtain metabolomics data sets due to their versatility, accessibility, and unique strengths. NMR requires minimal sample handling without the need for chromatography, is easily quantitative, and provides multiple means of metabolite identification, but is limited to detecting the most abundant metabolites (⩾1μM). Conversely, mass spectrometry has the ability to measure metabolites at very low concentrations (femtomolar to attomolar) and has a higher resolution (∼103-104) and dynamic range (∼103-104), but quantitation is a challenge and sample complexity may limit metabolite detection because of ion suppression. Consequently, liquid chromatography (LC) or gas chromatography (GC) is commonly employed in conjunction with MS, but this may lead to other sources of error. As a result, NMR and mass spectrometry are highly complementary, and combining the two techniques is likely to improve the overall quality of a study and enhance the coverage of the metabolome. While the majority of metabolomic studies use a single analytical source, there is a growing appreciation of the inherent value of combining NMR and MS for metabolomics. An overview of the current state of utilizing both NMR and MS for metabolomics will be presented.
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Affiliation(s)
- Darrell D Marshall
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States.
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Capati A, Ijare OB, Bezabeh T. Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics. MAGNETIC RESONANCE INSIGHTS 2017; 10:1178623X17694346. [PMID: 28579794 PMCID: PMC5428226 DOI: 10.1177/1178623x17694346] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2016] [Accepted: 01/25/2017] [Indexed: 12/23/2022]
Abstract
Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.
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Affiliation(s)
- Ana Capati
- College of Natural and Applied Sciences, University of Guam, Mangilao, GU, USA
| | - Omkar B Ijare
- Department of Chemistry, The University of Winnipeg, Winnipeg, MB, Canada
| | - Tedros Bezabeh
- College of Natural and Applied Sciences, University of Guam, Mangilao, GU, USA.,Department of Chemistry, The University of Winnipeg, Winnipeg, MB, Canada
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Hocher B, Adamski J. Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol 2017; 13:269-284. [PMID: 28262773 DOI: 10.1038/nrneph.2017.30] [Citation(s) in RCA: 214] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.
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Affiliation(s)
- Berthold Hocher
- Department of Basic Medicine, Medical College of Hunan University, 410006 Changsha, China
| | - Jerzy Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany
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Trivedi DK, Hollywood KA, Goodacre R. Metabolomics for the masses: The future of metabolomics in a personalized world. NEW HORIZONS IN TRANSLATIONAL MEDICINE 2017; 3:294-305. [PMID: 29094062 PMCID: PMC5653644 DOI: 10.1016/j.nhtm.2017.06.001] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 06/02/2017] [Accepted: 06/02/2017] [Indexed: 02/07/2023]
Abstract
Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.
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Affiliation(s)
| | | | - Royston Goodacre
- Manchester Institute of Biotechnology and School of Chemistry, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
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Brignardello J, Holmes E, Garcia-Perez I. Metabolic Phenotyping of Diet and Dietary Intake. ADVANCES IN FOOD AND NUTRITION RESEARCH 2017; 81:231-270. [PMID: 28317606 DOI: 10.1016/bs.afnr.2016.12.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Nutrition provides the building blocks for growth, repair, and maintenance of the body and is key to maintaining health. Exposure to fast foods, mass production of dietary components, and wider importation of goods have challenged the balance between diet and health in recent decades, and both scientists and clinicians struggle to characterize the relationship between this changing dietary landscape and human metabolism with its consequent impact on health. Metabolic phenotyping of foods, using high-density data-generating technologies to profile the biochemical composition of foods, meals, and human samples (pre- and postfood intake), can be used to map the complex interaction between the diet and human metabolism and also to assess food quality and safety. Here, we outline some of the techniques currently used for metabolic phenotyping and describe key applications in the food sciences, ending with a broad outlook at some of the newer technologies in the field with a view to exploring their potential to address some of the critical challenges in nutritional science.
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Affiliation(s)
- J Brignardello
- Computational and Systems Medicine, Imperial College London, London, United Kingdom
| | - E Holmes
- Computational and Systems Medicine, Imperial College London, London, United Kingdom
| | - I Garcia-Perez
- Nutrition and Dietetic Research Group, Imperial College London, London, United Kingdom.
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Lu Y, Chen C. Metabolomics: Bridging Chemistry and Biology in Drug Discovery and Development. ACTA ACUST UNITED AC 2017. [DOI: 10.1007/s40495-017-0083-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Ramakrishnan V, Luthria DL. Recent applications of NMR in food and dietary studies. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2017; 97:33-42. [PMID: 27435122 DOI: 10.1002/jsfa.7917] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Revised: 06/29/2016] [Accepted: 07/13/2016] [Indexed: 06/06/2023]
Abstract
Over the last decade, a wide variety of new foods have been introduced into the global marketplace, many with health benefits that exceed those of traditional foods. Simultaneously, a wide range of analytical technologies has evolved that allow greater capability for the determination of food composition. Nuclear magnetic resonance (NMR), traditionally a research tool used for structural elucidation, is now being used frequently for metabolomics and chemical fingerprinting. Its stability and inherent ease of quantification have been exploited extensively to identify and quantify bioactive components in foods and dietary supplements. In addition, NMR fingerprints have been used to differentiate cultivars, evaluate sensory properties of food and investigate the influence of growing conditions on food crops. Here we review the latest applications of NMR in food analysis. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- Venkatesh Ramakrishnan
- Food Composition Methods Development Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, US Department of Agriculture, Beltsville, MD, 20705, USA
| | - Devanand L Luthria
- Food Composition Methods Development Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, US Department of Agriculture, Beltsville, MD, 20705, USA
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McKay J, Tkáč I. Quantitative in vivo neurochemical profiling in humans: where are we now? Int J Epidemiol 2016; 45:1339-1350. [PMID: 27794521 DOI: 10.1093/ije/dyw235] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2016] [Indexed: 11/14/2022] Open
Abstract
Proton nuclear magnetic resonance spectroscopy of biofluids has become one of the key techniques for metabolic profiling and phenotyping. This technique has been widely used in a number of epidemiological studies and in a variety of health disorders. However, its utilization in brain disorders is limited due to the blood-brain barrier, which not only protects the brain from unwanted substances in the blood, but also substantially limits the potential of finding biomarkers for neurological disorders in serum. This review article focuses on the potential of localized in vivo proton magnetic resonance spectroscopy (1H-MRS) for non-invasive neurochemical profiling in the human brain. First, methodological aspects of 1H-MRS (data acquisition, processing and metabolite quantification) that are essential for reliable non-invasive neurochemical profiling are described. Second, the power of 1H-MRS-based neurochemical profiling is demonstrated using some examples of its application in neuroscience and neurology. Finally, the authors present their vision and propose necessary steps to establish 1H-MRS as a method suitable for large-scale neurochemical profiling in epidemiological research.
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Affiliation(s)
- Jessica McKay
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Ivan Tkáč
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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Fernandez O, Urrutia M, Bernillon S, Giauffret C, Tardieu F, Le Gouis J, Langlade N, Charcosset A, Moing A, Gibon Y. Fortune telling: metabolic markers of plant performance. Metabolomics 2016; 12:158. [PMID: 27729832 PMCID: PMC5025497 DOI: 10.1007/s11306-016-1099-1] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 08/16/2016] [Indexed: 02/01/2023]
Abstract
BACKGROUND In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC-MS, LC-MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained. AIM OF REVIEW (i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding. KEY MESSAGE Using examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance.
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Affiliation(s)
- Olivier Fernandez
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - Maria Urrutia
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
| | - Stéphane Bernillon
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | | | | | | | - Nicolas Langlade
- UMR LIPM, INRA, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France
| | - Alain Charcosset
- UMR GQE, INRA, CNRS, Université Paris Sud, AgroParisTech, Ferme du Moulon, 91190 Gif-Sur-Yvette, France
| | - Annick Moing
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRA, Centre INRA de Bordeaux, 71 av Edouard Bourlaux, 33140 Villenave d’Ornon, France
- Plateforme Métabolome Bordeaux, CGFB, MetaboHUB-PHENOME, 33140 Villenave d’Ornon, France
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Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA, Cascante M, Brennan L, Wishart DS, Oresic M, Hankemeier T, Broadhurst DI, Lane AN, Suhre K, Kastenmüller G, Sumner SJ, Thiele I, Fiehn O, Kaddurah-Daouk R. Metabolomics enables precision medicine: "A White Paper, Community Perspective". Metabolomics 2016; 12:149. [PMID: 27642271 PMCID: PMC5009152 DOI: 10.1007/s11306-016-1094-6] [Citation(s) in RCA: 368] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 08/08/2016] [Indexed: 01/12/2023]
Abstract
INTRODUCTION BACKGROUND TO METABOLOMICS Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.
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Affiliation(s)
- Richard D. Beger
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079 USA
| | - Warwick Dunn
- School of Biosciences, Phenome Centre Birmingham and Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Michael A. Schmidt
- Advanced Pattern Analysis and Countermeasures Group, Research Innovation Center, Colorado State University, Fort Collins, CO 80521 USA
| | - Steven S. Gross
- Department of Pharmacology, Weill Cornell Medical College, New York, NY 10021 USA
| | - Jennifer A. Kirwan
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain
- Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain
| | | | - David S. Wishart
- Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, AB Canada
| | - Matej Oresic
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
| | - Thomas Hankemeier
- Division of Analytical Biosciences and Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University & Netherlands Metabolomics Centre, Leiden, The Netherlands
| | | | - Andrew N. Lane
- Center for Environmental Systems Biochemistry, Department Toxicology and Cancer Biology, Markey Cancer Center, Lexington, KY USA
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, Oberschleißheim, Germany
| | - Susan J. Sumner
- Discovery Sciences, RTI International, Research Triangle Park, Durham, NC USA
| | - Ines Thiele
- University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis, Davis, CA USA
- Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rima Kaddurah-Daouk
- Psychiatry and Behavioral Sciences, Duke Internal Medicine and Duke Institute for Brain Sciences and Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Box 3903, Durham, NC 27710 USA
| | - for “Precision Medicine and Pharmacometabolomics Task Group”-Metabolomics Society Initiative
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079 USA
- School of Biosciences, Phenome Centre Birmingham and Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
- Advanced Pattern Analysis and Countermeasures Group, Research Innovation Center, Colorado State University, Fort Collins, CO 80521 USA
- Department of Pharmacology, Weill Cornell Medical College, New York, NY 10021 USA
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain
- Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain
- UCD Institute of Food and Health, UCD, Belfield, Dublin Ireland
- Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, AB Canada
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
- Division of Analytical Biosciences and Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University & Netherlands Metabolomics Centre, Leiden, The Netherlands
- School of Science, Edith Cowan University, Perth, Australia
- Center for Environmental Systems Biochemistry, Department Toxicology and Cancer Biology, Markey Cancer Center, Lexington, KY USA
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, Oberschleißheim, Germany
- Discovery Sciences, RTI International, Research Triangle Park, Durham, NC USA
- University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Campus Belval, Esch-Sur-Alzette, Luxembourg
- West Coast Metabolomics Center, UC Davis, Davis, CA USA
- Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia
- Psychiatry and Behavioral Sciences, Duke Internal Medicine and Duke Institute for Brain Sciences and Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Box 3903, Durham, NC 27710 USA
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Deng L, Gu H, Zhu J, Nagana Gowda GA, Djukovic D, Chiorean EG, Raftery D. Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls. Anal Chem 2016; 88:7975-83. [PMID: 27437783 DOI: 10.1021/acs.analchem.6b00885] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.
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Affiliation(s)
- Lingli Deng
- Department of Information Engineering, East China University of Technology , 418 Guanglan Avenue, Nanchang, Jiangxi Province 330013, China.,Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States
| | - Haiwei Gu
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States.,Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation, East China University of Technology , 418 Guanglan Avenue, Nanchang, Jiangxi Province 330013, China
| | - Jiangjiang Zhu
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States
| | - G A Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States
| | - Danijel Djukovic
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States
| | - E Gabriela Chiorean
- Department of Medicine, University of Washington , 825 Eastlake Avenue East, Seattle, Washington 98109, United States.,Indiana University Melvin and Bren Simon Cancer Center , 535 Barnhill Drive, Indianapolis, Indiana 46202, United States
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States.,Department of Chemistry, Purdue University , 560 Oval Drive, West Lafayette, Indiana 47907, United States.,Public Health Sciences Division, Fred Hutchinson Cancer Research Center , 1100 Fairview Avenue North, Seattle, Washington 98109, United States
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49
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RoyChoudhury S, Singh A, Gupta NJ, Srivastava S, Joshi MV, Chakravarty B, Chaudhury K. Repeated implantation failure versus repeated implantation success: discrimination at a metabolomic level. Hum Reprod 2016; 31:1265-74. [PMID: 27060172 DOI: 10.1093/humrep/dew064] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 03/03/2016] [Indexed: 12/14/2022] Open
Abstract
STUDY QUESTION Is there any difference at the serum metabolic level between women with recurrent implantation failure (RIF) and women with recurrent implantation success (RIS) when undergoing in vitro fertilization (IVF)? SUMMARY ANSWER Eight metabolites, including valine, adipic acid, l-lysine, creatine, ornithine, glycerol, d-glucose and urea, were found to be significantly up-regulated in women with RIF when compared with women with RIS. WHAT IS KNOWN ALREADY Despite transfer of three high-grade embryos per cycle, RIF following three or more consecutive IVF attempts occurs in a group of infertile women. Conversely, there is a group of women who undergo successful implantation each cycle, yet have a poor obstetric history. STUDY DESIGN, SIZE, DURATION This study was conducted over a period of 10 years (January 2004-October 2014). Groups of 28 women with RIF (age ≤40 years and BMI ≤28) and 24 women with RIS (age and BMI matched) were selected from couples with primary infertility reporting at the Institute of Reproductive Medicine, Kolkata, India. Women recruited in the RIF group had history of implantation failure in at least three consecutive IVF attempts, in which three embryos of high-grade quality were transferred in each cycle. PARTICIPANTS/MATERIALS, SETTING, METHODS Blood samples were collected from both the groups during the implantation window following overnight fasting for at least 10 h (7-10 days post ovulation). Samples were analyzed using a 700 MHz NMR spectrometer and acquired spectra were subjected to chemometric and statistical analysis. Serum levels of endothelial nitric oxide synthase (eNOS) were measured using an enzyme immunoassay technique. MAIN RESULTS AND THE ROLE OF CHANCE Valine, adipic acid, l-lysine, creatine, ornithine, glycerol, d-glucose and urea were found to be significantly down-regulated in women with RIS when compared with those with RIF, with fold change values of 0.81, 0.82, 0.79, 0.80, 0.78, 0.68, 0.76 and 0.74, respectively. Further, serum eNOS was found to be significantly lower in women with RIF when compared with RIS (P < 0.05), indicating possible impairment in nitric oxide production. Metabolites, mostly related to energy metabolism, lipid metabolism and the arginine metabolic pathway were found to be considerably altered and are likely to be associated with the RIF phenomenon. However, the interplay between these molecules in RIF is complex and holds merit for further exploration. LIMITATIONS, REASONS FOR CAUTION In-depth studies of the arginine metabolic pathway in endometrial tissues seem necessary to validate our findings. A limitation of the present study is that the metabolic level changes, eNOS and nitric oxide levels have not been investigated in the endometrial tissues of the two groups of women. It would be interesting to investigate whether there exists a direct link between metabolic dysregulation and genetic factors that affects implantation in RIF women. WIDER IMPLICATIONS OF THE FINDINGS We speculate that tissue metabolomics can provide an improved understanding of the metabolic dysfunction associated with RIF. The identification of serum metabolic marker(s) in women with RIS may help with strategies of early therapeutic intervention, which may improve the chances of implantation significantly in women otherwise susceptible to IVF failure. STUDY FUNDING/COMPETING INTERESTS One of the authors, S.R.C. acknowledges the Council of Scientific and Industrial Research (CSIR), Government of India [No: 9/81(1228)/14, EMR-I] for financial support.
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Affiliation(s)
- Sourav RoyChoudhury
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, India
| | - Apoorva Singh
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, India
| | | | - Sudha Srivastava
- National Facility for High-field NMR, Tata Institute of Fundamental Research, Mumbai, India
| | - Mamata V Joshi
- National Facility for High-field NMR, Tata Institute of Fundamental Research, Mumbai, India
| | | | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, India
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50
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Abstract
This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties.
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