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Raines NH, Leone DA, Amador JJ, Lopez-Pilarte D, Ramírez-Rubio O, Delgado IS, Francey LJ, Leibler JH, Asara JM, Scammell MK, Parikh SM, Brooks DR, Friedman DJ. Derangement in Nicotinamide Adenine Dinucleotide Metabolism is Observed During Acute Kidney Injury Among Male Agricultural Workers at Risk for Mesoamerican Nephropathy. Kidney Int Rep 2024; 9:2250-2259. [PMID: 39081728 PMCID: PMC11284402 DOI: 10.1016/j.ekir.2024.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 04/08/2024] [Indexed: 08/02/2024] Open
Abstract
Introduction Mesoamerican nephropathy (MeN) is a chronic kidney disease (CKD) which may be caused by recurrent acute kidney injury (AKI). We investigated urinary quinolinate-to-tryptophan ratio (Q/T), a validated marker of nicotinamide adenine dinucleotide (NAD+) biosynthesis that is elevated during ischemic and inflammatory AKI, in a sugarcane worker population in Nicaragua with high rates of MeN. Methods Among 693 male sugarcane workers studied, we identified 45 who developed AKI during the harvest season. We matched them 1:1 based on age and job category with 2 comparison groups: (i) "no kidney injury," active sugarcane workers with serum creatinine (sCr) <1.1 mg/dl; and (ii) "CKD," individuals no longer working in sugarcane due to their CKD, who had additional 1:1 matching for sCr. We measured urine metabolites using liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) and compared Q/T and other metabolic features between the AKI and comparison groups. Results Urine Q/T was significantly higher in workers with AKI than in those with no kidney injury (median interquartile Range [IQR]: 0.104 [0.074-0.167] vs. 0.060 [0.045-0.091], P < 0.0001) and marginally higher than in workers with CKD (0.086 [0.063-0.142], P = 0.059). Urine levels of the NAD+ precursor nicotinamide were lower in the AKI group than in comparison groups. Conclusion Workers at risk for MeN who develop AKI demonstrate features of impaired NAD+ biosynthesis, thereby providing new insights into the metabolic mechanisms of injury in this population. Therapeutic use of oral nicotinamide, which may ameliorate NAD+ biosynthetic derangement and fortify against kidney injury, should be investigated to prevent AKI in this setting.
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Affiliation(s)
- Nathan H. Raines
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Dominic A. Leone
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Juan Jose Amador
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Damaris Lopez-Pilarte
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Oriana Ramírez-Rubio
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| | - Iris S. Delgado
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Lauren J. Francey
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Jessica H. Leibler
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA
| | - John M. Asara
- Harvard Medical School, Boston, Massachusetts, USA
- Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Madeleine K. Scammell
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Samir M. Parikh
- Division of Nephrology, Department of Medicine, University of Texas Southwestern Medical School, Dallas, Texas, USA
| | - Daniel R. Brooks
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - David J. Friedman
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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Sakarin S, Rungsipipat A, Roytrakul S, Jaresitthikunchai J, Phaonakrop N, Charoenlappanit S, Thaisakun S, Surachetpong SD. Proteomic analysis of the serum in dogs with pulmonary hypertension secondary to myxomatous mitral valve disease: the preliminary study. Front Vet Sci 2024; 11:1327453. [PMID: 38596466 PMCID: PMC11002142 DOI: 10.3389/fvets.2024.1327453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 03/01/2024] [Indexed: 04/11/2024] Open
Abstract
Background Pulmonary hypertension (PH) is a common complication in dogs with myxomatous mitral valve disease (MMVD), characterized by elevated blood pressure in pulmonary artery. Echocardiography is a reliable technique for PH diagnosis in veterinary medicine. However, it is limited to use as an early detection method. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has found extensive application in the discovery of serum protein biomarkers for various diseases. The objective of this study was to identify serum proteins in healthy control dogs and MMVD dogs both with and without PH using LC-MS/MS. Materials and methods In this research, a total of 81 small-breed dogs participated, and they were categorized into three groups: the control (n = 28), MMVD (n = 24) and MMVD+PH (n = 29) groups. Serum samples were collected and analyzed by LC-MS/MS. Results Differentially expressed proteins were identified, and the upregulated and downregulated proteins in MMVD+PH group including Myomesin 1 (MYOM1) and Histone deacetylase 7 (HDAC7), Pleckstrin homology domain containing M3 (PLEKHM3), Diacylglycerol lipase alpha (DAGLA) and Tubulin tyrosine ligase like 6 (TTLL6) were selected as proteins of interest in MMVD dogs with PH. Conclusion Different types of proteins have been identified in healthy dogs and MMVD dogs with and without PH. Additional studies are needed to investigate the potential of these proteins as biomarkers for PH in dogs with MMVD.
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Affiliation(s)
- Siriwan Sakarin
- Faculty of Veterinary Science, Department of Veterinary Medicine, Center of Excellence for Companion Animal Cancer, Chulalongkorn University, Bangkok, Thailand
| | - Anudep Rungsipipat
- Faculty of Veterinary Science, Department of Pathology, Chulalongkorn University, Bangkok, Thailand
| | - Sittiruk Roytrakul
- Functional Proteomics Technology Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Janthima Jaresitthikunchai
- Functional Proteomics Technology Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Narumon Phaonakrop
- Functional Proteomics Technology Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Sawanya Charoenlappanit
- Functional Proteomics Technology Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Siriwan Thaisakun
- Functional Proteomics Technology Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, Thailand
| | - Sirilak Disatian Surachetpong
- Faculty of Veterinary Science, Department of Veterinary Medicine, Center of Excellence for Companion Animal Cancer, Chulalongkorn University, Bangkok, Thailand
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Zhong Y, Zhou L, Xu J, Huang H. Predicting prognosis outcomes of primary central nervous system lymphoma with high-dose methotrexate-based chemotherapeutic treatment using lipidomics. Neurooncol Adv 2024; 6:vdae119. [PMID: 39119277 PMCID: PMC11306931 DOI: 10.1093/noajnl/vdae119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2024] Open
Abstract
Background Primary central nervous system lymphoma (PCNSL) is a rare extranodal lymphomatous malignancy which is commonly treated with high-dose methotrexate (HD-MTX)-based chemotherapy. However, the prognosis outcome of HD-MTX-based treatment cannot be accurately predicted using the current prognostic scoring systems, such as the Memorial Sloan-Kettering Cancer Center (MSKCC) score. Methods We studied 2 cohorts of patients with PCNSL and applied lipidomic analysis to their cerebrospinal fluid (CSF) samples. After removing the batch effects and features engineering, we applied and compared several classic machine-learning models based on lipidomic data of CSF to predict the relapse of PCNSL in patients who were treated with HD-MTX-based chemotherapy. Results We managed to remove the batch effects and get the optimum features of each model. Finally, we found that Cox regression had the best prediction performance (AUC = 0.711) on prognosis outcomes. Conclusions We developed a Cox regression model based on lipidomic data, which could effectively predict PCNSL patient prognosis before the HD-MTX-based chemotherapy treatments.
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Affiliation(s)
- Yi Zhong
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
| | - Liying Zhou
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
| | - Jingshen Xu
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
| | - He Huang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China
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Mattoli L, Gianni M, Burico M. Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products. MASS SPECTROMETRY REVIEWS 2023; 42:1358-1396. [PMID: 35238411 DOI: 10.1002/mas.21773] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 11/16/2021] [Accepted: 02/11/2022] [Indexed: 06/07/2023]
Abstract
Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.
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Affiliation(s)
- Luisa Mattoli
- Department of Metabolomics & Analytical Sciences, Aboca SpA Società Agricola, Sansepolcro, AR, Italy
| | - Mattia Gianni
- Department of Metabolomics & Analytical Sciences, Aboca SpA Società Agricola, Sansepolcro, AR, Italy
| | - Michela Burico
- Department of Metabolomics & Analytical Sciences, Aboca SpA Società Agricola, Sansepolcro, AR, Italy
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Muli S, Brachem C, Alexy U, Schmid M, Oluwagbemigun K, Nöthlings U. Exploring the association of physical activity with the plasma and urine metabolome in adolescents and young adults. Nutr Metab (Lond) 2023; 20:23. [PMID: 37020289 PMCID: PMC10074825 DOI: 10.1186/s12986-023-00742-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/29/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Regular physical activity elicits many health benefits. However, the underlying molecular mechanisms through which physical activity influences overall health are less understood. Untargeted metabolomics enables system-wide mapping of molecular perturbations which may lend insights into physiological responses to regular physical activity. In this study, we investigated the associations of habitual physical activity with plasma and urine metabolome in adolescents and young adults. METHODS This cross-sectional study included participants from the DONALD (DOrtmund Nutritional and Anthropometric Longitudinally Designed) study with plasma samples n = 365 (median age: 18.4 (18.1, 25.0) years, 58% females) and 24 h urine samples n = 215 (median age: 18.1 (17.1, 18.2) years, 51% females). Habitual physical activity was assessed using a validated Adolescent Physical Activity Recall Questionnaire. Plasma and urine metabolite concentrations were determined using ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) methods. In a sex-stratified analysis, we conducted principal component analysis (PCA) to reduce the dimensionality of metabolite data and to create metabolite patterns. Multivariable linear regression models were then applied to assess the associations between self-reported physical activity (metabolic equivalent of task (MET)-hours per week) with single metabolites and metabolite patterns, adjusted for potential confounders and controlling the false discovery rate (FDR) at 5% for each set of regressions. RESULTS Habitual physical activity was positively associated with the "lipid, amino acids and xenometabolite" pattern in the plasma samples of male participants only (β = 1.02; 95% CI: 1.01, 1.04, p = 0.001, adjusted p = 0.042). In both sexes, no association of physical activity with single metabolites in plasma and urine and metabolite patterns in urine was found (all adjusted p > 0.05). CONCLUSIONS Our explorative study suggests that habitual physical activity is associated with alterations of a group of metabolites reflected in the plasma metabolite pattern in males. These perturbations may lend insights into some of underlying mechanisms that modulate effects of physical activity.
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Affiliation(s)
- Samuel Muli
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany.
| | - Christian Brachem
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Ute Alexy
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Matthias Schmid
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Kolade Oluwagbemigun
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Ute Nöthlings
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
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Raines NH, Leone DA, O’Callaghan-Gordo C, Ramirez-Rubio O, Amador JJ, Lopez Pilarte D, Delgado IS, Leibler JH, Embade N, Gil-Redondo R, Bruzzone C, Bizkarguenaga M, Scammell MK, Parikh SM, Millet O, Brooks DR, Friedman DJ. Metabolic Features of Increased Gut Permeability, Inflammation, and Altered Energy Metabolism Distinguish Agricultural Workers at Risk for Mesoamerican Nephropathy. Metabolites 2023; 13:325. [PMID: 36984765 PMCID: PMC10058628 DOI: 10.3390/metabo13030325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/10/2023] [Accepted: 02/18/2023] [Indexed: 02/24/2023] Open
Abstract
Mesoamerican nephropathy (MeN) is a form of chronic kidney disease found predominantly in young men in Mesoamerica. Strenuous agricultural labor is a consistent risk factor for MeN, but the pathophysiologic mechanism leading to disease is poorly understood. We compared the urine metabolome among men in Nicaragua engaged in sugarcane harvest and seed cutting (n = 117), a group at high risk for MeN, against three referents: Nicaraguans working less strenuous jobs at the same sugarcane plantations (n = 78); Nicaraguans performing non-agricultural work (n = 102); and agricultural workers in Spain (n = 78). Using proton nuclear magnetic resonance, we identified 136 metabolites among participants. Our non-hypothesis-based approach identified distinguishing urine metabolic features in the high-risk group, revealing increased levels of hippurate and other gut-derived metabolites and decreased metabolites related to central energy metabolism when compared to referent groups. Our complementary hypothesis-based approach, focused on nicotinamide adenine dinucleotide (NAD+) related metabolites, and revealed a higher kynurenate/tryptophan ratio in the high-risk group (p = 0.001), consistent with a heightened inflammatory state. Workers in high-risk occupations are distinguishable by urinary metabolic features that suggest increased gut permeability, inflammation, and altered energy metabolism. Further study is needed to explore the pathophysiologic implications of these findings.
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Affiliation(s)
- Nathan H. Raines
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Dominick A. Leone
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - Cristina O’Callaghan-Gordo
- Faculty of Health Sciences, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
- ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
| | - Oriana Ramirez-Rubio
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
- ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain
| | - Juan José Amador
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - Damaris Lopez Pilarte
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - Iris S. Delgado
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - Jessica H. Leibler
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Nieves Embade
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Rubén Gil-Redondo
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Chiara Bruzzone
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Maider Bizkarguenaga
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Madeleine K. Scammell
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - Samir M. Parikh
- Division of Nephrology, Department of Medicine, University of Texas Southwestern Medical School, Dallas, TX 75390, USA
| | - Oscar Millet
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
- CIBERehd, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Daniel R. Brooks
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
| | - David J. Friedman
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
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Borges RM, Gouveia GJ, das Chagas FO. Advances in Microbial NMR Metabolomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1439:123-147. [PMID: 37843808 DOI: 10.1007/978-3-031-41741-2_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.
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Affiliation(s)
- Ricardo Moreira Borges
- Instituto de Pesquisas de Produtos Naturais Walter Mors, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gonçalo Jorge Gouveia
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, USA
| | - Fernanda Oliveira das Chagas
- Instituto de Pesquisas de Produtos Naturais Walter Mors, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
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8
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Du X, Aristizabal-Henao JJ, Garrett TJ, Brochhausen M, Hogan WR, Lemas DJ. A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research. Metabolites 2022; 12:87. [PMID: 35050209 PMCID: PMC8779534 DOI: 10.3390/metabo12010087] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 12/25/2021] [Accepted: 01/10/2022] [Indexed: 12/15/2022] Open
Abstract
Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.
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Affiliation(s)
- Xinsong Du
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (X.D.); (W.R.H.)
| | | | - Timothy J. Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL 32610, USA;
| | - Mathias Brochhausen
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - William R. Hogan
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (X.D.); (W.R.H.)
| | - Dominick J. Lemas
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (X.D.); (W.R.H.)
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Zamora Obando HR, Duarte GHB, Simionato AVC. Metabolomics Data Treatment: Basic Directions of the Full Process. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1336:243-264. [PMID: 34628635 DOI: 10.1007/978-3-030-77252-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.
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Affiliation(s)
- Hans Rolando Zamora Obando
- Department of Analytical Chemistry, Institute of Chemistry, University of Campinas, Campinas, SP, Brazil
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10
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Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M, Gauthier C, Jacques PÉ, Li S, Xia J. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res 2021; 49:W388-W396. [PMID: 34019663 PMCID: PMC8265181 DOI: 10.1093/nar/gkab382] [Citation(s) in RCA: 2116] [Impact Index Per Article: 705.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/17/2021] [Accepted: 04/27/2021] [Indexed: 12/31/2022] Open
Abstract
Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.
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Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jasmine Chong
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | | | - Le Chang
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Michel Barrette
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Carol Gauthier
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Pierre-Étienne Jacques
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Department of Animal Science, McGill University, Montreal, Quebec, Canada
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11
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Fu J, Zhang Y, Liu J, Lian X, Tang J, Zhu F. Pharmacometabonomics: data processing and statistical analysis. Brief Bioinform 2021; 22:6236068. [PMID: 33866355 DOI: 10.1093/bib/bbab138] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 02/09/2021] [Accepted: 03/23/2021] [Indexed: 12/14/2022] Open
Abstract
Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Ying Zhang
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Jin Liu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Xichen Lian
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Jing Tang
- Department of Bioinformatics in Chongqing Medical University, China
| | - Feng Zhu
- College of Pharmaceutical Sciences in Zhejiang University, China
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