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Alcolea JA, Donat-Vargas C, Chatziioannou AC, Keski-Rahkonen P, Robinot N, Molina AJ, Amiano P, Gómez-Acebo I, Castaño-Vinyals G, Maitre L, Chadeau-Hyam M, Dagnino S, Cheng SL, Scalbert A, Vineis P, Kogevinas M, Villanueva CM. Metabolomic Signatures of Exposure to Nitrate and Trihalomethanes in Drinking Water and Colorectal Cancer Risk in a Spanish Multicentric Study (MCC-Spain). Environ Sci Technol 2023; 57:19316-19329. [PMID: 37962559 DOI: 10.1021/acs.est.3c05814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
We investigated the metabolomic profile associated with exposure to trihalomethanes (THMs) and nitrate in drinking water and with colorectal cancer risk in 296 cases and 295 controls from the Multi Case-Control Spain project. Untargeted metabolomic analysis was conducted in blood samples using ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. A variety of univariate and multivariate association analyses were conducted after data quality control, normalization, and imputation. Linear regression and partial least-squares analyses were conducted for chloroform, brominated THMs, total THMs, and nitrate among controls and for case-control status, together with a N-integration model discriminating colorectal cancer cases from controls through interrogation of correlations between the exposure variables and the metabolomic features. Results revealed a total of 568 metabolomic features associated with at least one water contaminant or colorectal cancer. Annotated metabolites and pathway analysis suggest a number of pathways as potentially involved in the link between exposure to these water contaminants and colorectal cancer, including nicotinamide, cytochrome P-450, and tyrosine metabolism. These findings provide insights into the underlying biological mechanisms and potential biomarkers associated with water contaminant exposure and colorectal cancer risk. Further research in this area is needed to better understand the causal relationship and the public health implications.
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Affiliation(s)
- Jose A Alcolea
- ISGlobal, c/Dr. Aiguader 88, Barcelona 08003, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Avenida Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), c/Doctor Aiguader 88, Barcelona 08003, Spain
| | - Carolina Donat-Vargas
- ISGlobal, c/Dr. Aiguader 88, Barcelona 08003, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Avenida Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), c/Doctor Aiguader 88, Barcelona 08003, Spain
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | | | - Pekka Keski-Rahkonen
- International Agency for Research on Cancer, 25 avenue Tony Garnier, CS 90627 69366, Lyon, France
| | - Nivonirina Robinot
- International Agency for Research on Cancer, 25 avenue Tony Garnier, CS 90627 69366, Lyon, France
| | - Antonio José Molina
- Research Group in Gene - Environment and Health Interactions (GIIGAS)/Institute of Biomedicine (IBIOMED), Universidad de León, Campus Universitario de Vegazana, León 24071, Spain
- Faculty of Health Sciences, Department of Biomedical Sciences, Area of Preventive Medicine and Public Health, Universidad de León, Campus Universitario de Vegazana, León 24071, Spain
| | - Pilar Amiano
- CIBER Epidemiología y Salud Pública (CIBERESP), Avenida Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid 28029, Spain
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa; BioGipuzkoa (BioDonostia) Health Research Institute, San Sebastián 20013, Spain
| | - Inés Gómez-Acebo
- CIBER Epidemiología y Salud Pública (CIBERESP), Avenida Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid 28029, Spain
- Universidad de Cantabria-IDIVAL, Avenida Cardenal Herrera Oria S/N, Santander 39011, Spain
| | - Gemma Castaño-Vinyals
- ISGlobal, c/Dr. Aiguader 88, Barcelona 08003, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Avenida Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), c/Doctor Aiguader 88, Barcelona 08003, Spain
- IMIM (Hospital del Mar Medical Research Institute), c/Doctor Aiguader 88, Barcelona 08003, Spain
| | - Lea Maitre
- ISGlobal, c/Dr. Aiguader 88, Barcelona 08003, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Avenida Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), c/Doctor Aiguader 88, Barcelona 08003, Spain
| | - Marc Chadeau-Hyam
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, United Kingdom
| | - Sonia Dagnino
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, United Kingdom
- Transporters in Imaging and Radiotherapy in Oncology (TIRO), School of Medicine, Direction de la Recherche Fondamentale (DRF), Institut des Sciences du Vivant Frédéric Joliot, Commissariat à l'Energie Atomique et aux Énergies Alternatives (CEA), Université Côte d'Azur (UCA), 28 Avenue de Valombrose, Nice 06107, France
| | - Sibo Lucas Cheng
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, United Kingdom
| | - Augustin Scalbert
- International Agency for Research on Cancer, 25 avenue Tony Garnier, CS 90627 69366, Lyon, France
| | - Paolo Vineis
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, United Kingdom
| | - Manolis Kogevinas
- ISGlobal, c/Dr. Aiguader 88, Barcelona 08003, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Avenida Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), c/Doctor Aiguader 88, Barcelona 08003, Spain
- IMIM (Hospital del Mar Medical Research Institute), c/Doctor Aiguader 88, Barcelona 08003, Spain
| | - Cristina M Villanueva
- ISGlobal, c/Dr. Aiguader 88, Barcelona 08003, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Avenida Monforte de Lemos, 3-5, Pabellón 11, Planta 0, Madrid 28029, Spain
- Universitat Pompeu Fabra (UPF), c/Doctor Aiguader 88, Barcelona 08003, Spain
- IMIM (Hospital del Mar Medical Research Institute), c/Doctor Aiguader 88, Barcelona 08003, Spain
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Fan X, Gao X, Deng Y, Ma B, Liu J, Zhang Z, Zhang D, Yang Y, Wang C, He B, Nie Q, Ye Z, Liu P, Wen J. Untargeted plasma metabolome identifies biomarkers in patients with extracranial arteriovenous malformations. Front Physiol 2023; 14:1207390. [PMID: 37727659 PMCID: PMC10505742 DOI: 10.3389/fphys.2023.1207390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/09/2023] [Indexed: 09/21/2023] Open
Abstract
Objective: This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM). Method: Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve. Result: In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and protein translation. Through machine learning algorithms, nine metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid, and dihydrojasmonic acid. Conclusion: Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.
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Affiliation(s)
- Xueqiang Fan
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Xixi Gao
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Yisen Deng
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Bo Ma
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Jingwen Liu
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Zhaohua Zhang
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Dingkai Zhang
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Yuguang Yang
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Cheng Wang
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Bin He
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Qiangqiang Nie
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Zhidong Ye
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Peng Liu
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Jianyan Wen
- Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
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Parker EJ, Billane KC, Austen N, Cotton A, George RM, Hopkins D, Lake JA, Pitman JK, Prout JN, Walker HJ, Williams A, Cameron DD. Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools. Metabolites 2023; 13:metabo13040463. [PMID: 37110122 PMCID: PMC10142740 DOI: 10.3390/metabo13040463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.
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Affiliation(s)
| | - Kathryn C Billane
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Nichola Austen
- Department of Biology, University of Oxford, Oxford OX1 3RB, UK
| | - Anne Cotton
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Rachel M George
- biOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK
| | - David Hopkins
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
| | - Janice A Lake
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
| | - James K Pitman
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - James N Prout
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Heather J Walker
- biOMICS Mass Spectrometry Centre, University of Sheffield, Sheffield S10 2TN, UK
| | - Alex Williams
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Duncan D Cameron
- Department of Earth and Environmental Sciences, University of Manchester, Manchester M13 9PL, UK
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Naik VD, Ramadoss J. Untargeted and Targeted Blood Lipidomic Signature Profile of Gestational Alcohol Exposure. Nutrients 2023; 15:1411. [PMID: 36986141 PMCID: PMC10051993 DOI: 10.3390/nu15061411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Alcohol consumption has a close relationship with blood lipid levels in a nonpregnant state, with a myriad of effects on the liver; however, little is known about the interaction of alcohol and lipids in the context of fetal alcohol spectrum disorders (FASD). We herein aimed to determine the effect of alcohol on the lipid profile in a pregnant rat model, with a focus on FASD. Dry blood spots (50 µL) were obtained from rat maternal blood collected on gestational day (GD) 20, two hours after the last binge alcohol exposure (4.5 g/kg, GD 5-10; 6 g/kg, GD 11-20). The samples were then analyzed using high-throughput untargeted and targeted lipid profiling via liquid chromatography-tandem mass spectrometry (LC-MS/MS). In untargeted lipidomics, 73 of 315 identified lipids were altered in the alcohol group compared to the pair-fed controls; 67 were downregulated and 6 were upregulated. In targeted analysis, 57 of the 260 studied lipid subspecies were altered, including Phosphatidylcholine (PC), Phosphatidylethanolamine (PE), Phosphatidylglycerol (PG), Phosphatidic Acid (PA), Phosphatidylinositol (PI), and Phosphatidylserine (PS); 36 of these were downregulated and 21 lipid subspecies were upregulated. These findings suggest alcohol-induced dysregulation of lipids in the maternal blood of rats and provide novel insights into possible FASD mechanisms.
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Affiliation(s)
- Vishal D. Naik
- Department of Obstetrics & Gynecology, C.S. Mott Center for Human Growth and Development, School of Medicine, Wayne State University, Detroit, MI 48201, USA
| | - Jayanth Ramadoss
- Department of Obstetrics & Gynecology, C.S. Mott Center for Human Growth and Development, School of Medicine, Wayne State University, Detroit, MI 48201, USA
- Department of Physiology, School of Medicine, Wayne State University, Detroit, MI 48201, USA
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Sussman EM, Oktem B, Isayeva IS, Liu J, Wickramasekara S, Chandrasekar V, Nahan K, Shin HY, Zheng J. Chemical Characterization and Non-targeted Analysis of Medical Device Extracts: A Review of Current Approaches, Gaps, and Emerging Practices. ACS Biomater Sci Eng 2022; 8:939-963. [PMID: 35171560 DOI: 10.1021/acsbiomaterials.1c01119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The developers of medical devices evaluate the biocompatibility of their device prior to FDA's review and subsequent introduction to the market. Chemical characterization, described in ISO 10993-18:2020, can generate information for toxicological risk assessment and is an alternative approach for addressing some biocompatibility end points (e.g., systemic toxicity, genotoxicity, carcinogenicity, reproductive/developmental toxicity) that can reduce the time and cost of testing and the need for animal testing. Additionally, chemical characterization can be used to determine whether modifications to the materials and manufacturing processes alter the chemistry of a patient-contacting device to an extent that could impact device safety. Extractables testing is one approach to chemical characterization that employs combinations of non-targeted analysis, non-targeted screening, and/or targeted analysis to establish the identities and quantities of the various chemical constituents that can be released from a device. Due to the difficulty in obtaining a priori information on all the constituents in finished devices, information generation strategies in the form of analytical chemistry testing are often used. Identified and quantified extractables are then assessed using toxicological risk assessment approaches to determine if reported quantities are sufficiently low to overcome the need for further chemical analysis, biological evaluation of select end points, or risk control. For extractables studies to be useful as a screening tool, comprehensive and reliable non-targeted methods are needed. Although non-targeted methods have been adopted by many laboratories, they are laboratory-specific and require expensive analytical instruments and advanced technical expertise to perform. In this Perspective, we describe the elements of extractables studies and provide an overview of the current practices, identified gaps, and emerging practices that may be adopted on a wider scale in the future. This Perspective is outlined according to the steps of an extractables study: information gathering, extraction, extract sample processing, system selection, qualification, quantification, and identification.
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Affiliation(s)
- Eric M Sussman
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Berk Oktem
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Irada S Isayeva
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jinrong Liu
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Samanthi Wickramasekara
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Vaishnavi Chandrasekar
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Keaton Nahan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Hainsworth Y Shin
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jiwen Zheng
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland 20993, United States
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Fernández-Ochoa Á, Cádiz-Gurrea MDLL, Fernández-Moreno P, Rojas-García A, Arráez-Román D, Segura-Carretero A. Recent Analytical Approaches for the Study of Bioavailability and Metabolism of Bioactive Phenolic Compounds. Molecules 2022; 27:777. [PMID: 35164041 PMCID: PMC8838714 DOI: 10.3390/molecules27030777] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 01/21/2022] [Accepted: 01/23/2022] [Indexed: 12/14/2022] Open
Abstract
The study of the bioavailability of bioactive compounds is a fundamental step for the development of applications based on them, such as nutraceuticals, functional foods or cosmeceuticals. It is well-known that these compounds can undergo metabolic reactions before reaching therapeutic targets, which may also affect their bioactivity and possible applications. All recent studies that have focused on bioavailability and metabolism of phenolic and terpenoid compounds have been developed because of the advances in analytical chemistry and metabolomics approaches. The purpose of this review is to show the role of analytical chemistry and metabolomics in this field of knowledge. In this context, the different steps of the analytical chemistry workflow (design study, sample treatment, analytical techniques and data processing) applied in bioavailability and metabolism in vivo studies are detailed, as well as the most relevant results obtained from them.
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Affiliation(s)
- Álvaro Fernández-Ochoa
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
- Berlin Institute of Health, Metabolomics Platform, 10178 Berlin, Germany
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Fuentenueva s/n, E-18071 Granada, Spain; (M.d.l.L.C.-G.); (P.F.-M.); (A.R.-G.); (A.S.-C.)
| | - María de la Luz Cádiz-Gurrea
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Fuentenueva s/n, E-18071 Granada, Spain; (M.d.l.L.C.-G.); (P.F.-M.); (A.R.-G.); (A.S.-C.)
| | - Patricia Fernández-Moreno
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Fuentenueva s/n, E-18071 Granada, Spain; (M.d.l.L.C.-G.); (P.F.-M.); (A.R.-G.); (A.S.-C.)
| | - Alejandro Rojas-García
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Fuentenueva s/n, E-18071 Granada, Spain; (M.d.l.L.C.-G.); (P.F.-M.); (A.R.-G.); (A.S.-C.)
| | - David Arráez-Román
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Fuentenueva s/n, E-18071 Granada, Spain; (M.d.l.L.C.-G.); (P.F.-M.); (A.R.-G.); (A.S.-C.)
| | - Antonio Segura-Carretero
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Fuentenueva s/n, E-18071 Granada, Spain; (M.d.l.L.C.-G.); (P.F.-M.); (A.R.-G.); (A.S.-C.)
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Margham J, McAdam K, Cunningham A, Porter A, Fiebelkorn S, Mariner D, Digard H, Proctor C. The Chemical Complexity of e-Cigarette Aerosols Compared With the Smoke From a Tobacco Burning Cigarette. Front Chem 2021; 9:743060. [PMID: 34660535 PMCID: PMC8514950 DOI: 10.3389/fchem.2021.743060] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 09/10/2021] [Indexed: 11/13/2022] Open
Abstract
Background: As e-cigarette popularity has increased, there is growing evidence to suggest that while they are highly likely to be considerably less harmful than cigarettes, their use is not free of risk to the user. There is therefore an ongoing need to characterise the chemical composition of e-cigarette aerosols, as a starting point in characterising risks associated with their use. This study examined the chemical complexity of aerosols generated by an e-cigarette containing one unflavored and three flavored e-liquids. A combination of targeted and untargeted chemical analysis approaches was used to examine the number of compounds comprising the aerosol. Contributions of e-liquid flavors to aerosol complexity were investigated, and the sources of other aerosol constituents sought. Emissions of 98 aerosol toxicants were quantified and compared to those in smoke from a reference tobacco cigarette generated under two different smoking regimes. Results: Combined untargeted and targeted aerosol analyses identified between 94 and 139 compounds in the flavored aerosols, compared with an estimated 72-79 in the unflavored aerosol. This is significantly less complex (by 1-2 orders of magnitude) than the reported composition of cigarette smoke. Combining both types of analysis identified 5-12 compounds over and above those found by untargeted analysis alone. Gravimetrically, 89-99% of the e-cigarette aerosol composition was composed of glycerol, propylene glycol, water and nicotine, and around 3% comprised other, more minor, constituents. Comparable data for the Ky3R4F reference tobacco cigarette pointed to 58-76% of cigarette smoke "tar" being composed of minor constituents. Levels of the targeted toxicants in the e-cigarette aerosols were significantly lower than those in cigarette smoke, with 68.5->99% reductions under ISO 3308 puffing conditions and 88.4->99% reductions under ISO 20778 (intense) conditions; reductions against the WHO TobReg 9 priority list were around 99%. Conclusion: These analyses showed that the e-cigarette aerosols contain fewer compounds and at significantly lower concentrations than cigarette smoke. The chemical diversity of an e-cigarette aerosol is strongly impacted by the choice of e-liquid ingredients.
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Affiliation(s)
- J. Margham
- Group Research and Development, British American Tobacco, Southampton, United Kingdom
| | - K. McAdam
- McAdam Scientific Ltd., Eastleigh, United Kingdom
| | - A. Cunningham
- Group Research and Development, British American Tobacco, Southampton, United Kingdom
| | - A. Porter
- Independent Researcher, Montreal, QC, Canada
| | - S. Fiebelkorn
- Group Research and Development, British American Tobacco, Southampton, United Kingdom
| | - D. Mariner
- Mariner Science Ltd., Salisbury, United Kingdom
| | - H. Digard
- Group Research and Development, British American Tobacco, Southampton, United Kingdom
| | - C. Proctor
- DoctorProctorScience Ltd., Ascot, United Kingdom
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Allwood JW, Williams A, Uthe H, van Dam NM, Mur LAJ, Grant MR, Pétriacq P. Unravelling Plant Responses to Stress-The Importance of Targeted and Untargeted Metabolomics. Metabolites 2021; 11:558. [PMID: 34436499 PMCID: PMC8398504 DOI: 10.3390/metabo11080558] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/10/2021] [Accepted: 08/16/2021] [Indexed: 12/19/2022] Open
Abstract
Climate change and an increasing population, present a massive global challenge with respect to environmentally sustainable nutritious food production. Crop yield enhancements, through breeding, are decreasing, whilst agricultural intensification is constrained by emerging, re-emerging, and endemic pests and pathogens, accounting for ~30% of global crop losses, as well as mounting abiotic stress pressures, due to climate change. Metabolomics approaches have previously contributed to our knowledge within the fields of molecular plant pathology and plant-insect interactions. However, these remain incredibly challenging targets, due to the vast diversity in metabolite volatility and polarity, heterogeneous mixtures of pathogen and plant cells, as well as rapid rates of metabolite turn-over. Unravelling the systematic biochemical responses of plants to various individual and combined stresses, involves monitoring signaling compounds, secondary messengers, phytohormones, and defensive and protective chemicals. This demands both targeted and untargeted metabolomics approaches, as well as a range of enzymatic assays, protein assays, and proteomic and transcriptomic technologies. In this review, we focus upon the technical and biological challenges of measuring the metabolome associated with plant stress. We illustrate the challenges, with relevant examples from bacterial and fungal molecular pathologies, plant-insect interactions, and abiotic and combined stress in the environment. We also discuss future prospects from both the perspective of key innovative metabolomic technologies and their deployment in breeding for stress resistance.
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Affiliation(s)
- James William Allwood
- Environmental and Biochemical Sciences, James Hutton Institute, Errol Road, Invergowrie, Dundee DD2 5DA, UK
| | - Alex Williams
- School of Earth and Environmental Sciences, The University of Manchester, Oxford Road, Manchester M13 9PT, UK;
- Department of Animal and Plant Sciences, Biosciences, The University of Sheffield Western Bank, Sheffield S10 2TN, UK
| | - Henriette Uthe
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Molecular Interaction Ecology Group, Friedrich-Schiller University Jena, Puschstr. 4, 04103 Leipzig, Germany; (H.U.); (N.M.v.D.)
| | - Nicole M. van Dam
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Molecular Interaction Ecology Group, Friedrich-Schiller University Jena, Puschstr. 4, 04103 Leipzig, Germany; (H.U.); (N.M.v.D.)
| | - Luis A. J. Mur
- Institute of Biological, Environmental and Rural Sciences (IBERS), Edward Llwyd Building, Aberystwyth University, Aberystwyth SY23 3DA, UK;
| | - Murray R. Grant
- Gibbet Hill Campus, School of Life Sciences, The University of Warwick, Coventry CV4 7AL, UK;
| | - Pierre Pétriacq
- UMR 1332 Fruit Biology and Pathology, Centre INRAE de Nouvelle Aquitaine Bordeaux, University of Bordeaux, 33140 Villenave d’Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, Centre INRAE de Nouvelle Aquitaine-Bordeaux, 33140 Villenave d’Ornon, France
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9
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Bourdin-Pintueles A, Galineau L, Nadal-Desbarats L, Dupuy C, Bodard S, Busson J, Lefèvre A, Emond P, Mavel S. Maternal Rat Metabolomics: Amniotic Fluid and Placental Metabolic Profiling Workflows. J Proteome Res 2021; 20:3853-3864. [PMID: 34282913 DOI: 10.1021/acs.jproteome.1c00145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Studying the metabolome of specific gestational compartments is of growing interest in the context of fetus developmental disorders. However, the metabolomes of the placenta and amniotic fluid (AF) are poorly characterized. Therefore, we present the validation of a fingerprinting methodology. Using pregnant rats, we performed exhaustive and robust extractions of metabolites in the AF and lipids and more polar metabolites in the placenta. For the AF, we compared the extraction capabilities of methanol (MeOH), acetonitrile (ACN), and a mixture of both. For the placenta, we compared (i) the extraction capabilities of dichloromethane, methyl t-butyl ether (MTBE), and butanol, along with (ii) the impact of lyophilization of the placental tissue. Analyses were performed on a C18 and hydrophilic interaction liquid chromatography combined with high-resolution mass spectrometry. The efficiency and the robustness of the extractions were compared based on the number of the features or metabolites (for untargeted or targeted approach, respectively), their mean total intensity, and their coefficient of variation (% CV). The extraction capabilities of MeOH and ACN on the AF metabolome were equivalent. Lyophilization also had no significant impact and usefulness on the placental tissue metabolome profiling. Considering the placental lipidome, MTBE extraction was more informative because it allowed extraction of a slightly higher number of lipids, in higher concentration. This proof-of-concept study assessing the metabolomics and lipidomics of the AF and the placenta revealed changes in both metabolisms, at two different stages of rat gestation, and allowed a detailed prenatal metabolic fingerprinting.
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Affiliation(s)
| | - Laurent Galineau
- UMR 1253 iBrain, Université de Tours, Inserm, Tours 37000, France
| | | | - Camille Dupuy
- UMR 1253 iBrain, Université de Tours, Inserm, Tours 37000, France
| | - Sylvie Bodard
- UMR 1253 iBrain, Université de Tours, Inserm, Tours 37000, France
| | - Julie Busson
- UMR 1253 iBrain, Université de Tours, Inserm, Tours 37000, France
| | - Antoine Lefèvre
- UMR 1253 iBrain, Université de Tours, Inserm, Tours 37000, France
| | - Patrick Emond
- UMR 1253 iBrain, Université de Tours, Inserm, Tours 37000, France.,CHRU de Tours, Service de Médecine Nucléaire In Vitro, Tours 37000, France
| | - Sylvie Mavel
- UMR 1253 iBrain, Université de Tours, Inserm, Tours 37000, France
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10
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Spieß L, de Peinder P, van den Bijgaart H. Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms. Foods 2021; 10:1111. [PMID: 34069770 DOI: 10.3390/foods10051111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 11/16/2022] Open
Abstract
Fourier-transform mid-infrared spectrometry is an attractive technology for screening adulterated liquid milk products. So far, studies on how infrared spectroscopy can be used to screen spectra for atypical milk composition have either used targeted methods to test for specific adulterants, or have used untargeted screening methods that do not reveal in what way the spectra are atypical. In this study, we evaluate the potential of combining untargeted screening methods with cluster algorithms to indicate in what way a spectrum is atypical and, if possible, why. We found that a combination of untargeted screening methods and cluster algorithms can reveal meaningful and generalizable categories of atypical milk spectra. We demonstrate that spectral information (e.g., the compositional milk profile) and meta-data associated with their acquisition (e.g., at what date and which instrument) can be used to understand in what way the milk is atypical and how it can be used to form hypotheses about the underlying causes. Thereby, it was indicated that atypical milk screening can serve as a valuable complementary quality assurance tool in routine FTIR milk analysis.
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11
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Zhu H, Abdullah AS, He J, Xi J, Mao Y, Feng Y, Xiao Q, Zheng P. Untargeted Urinary Metabolomics and Children's Exposure to Secondhand Smoke: The Influence of Individual Differences. Int J Environ Res Public Health 2021; 18:E710. [PMID: 33467557 DOI: 10.3390/ijerph18020710] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 12/30/2020] [Accepted: 01/09/2021] [Indexed: 12/26/2022]
Abstract
Children’s exposure to secondhand smoke (SHS) is a severe public health problem. There is still a lack of evidence regarding panoramic changes in children’s urinary metabolites induced by their involuntary exposure to SHS, and few studies have considered individual differences. This study aims to clarify the SHS-induced changes in urinary metabolites in preschool children by using cross-sectional and longitudinal metabolomics analyses. Urinary metabolites were quantified by using untargeted ultra high-performance liquid chromatography-mass spectrometry (UPLC(c)-MS/MS). Urine cotinine-measured SHS exposure was examined to determine the exposure level. A cross-sectional study including 17 children in a low-exposure group, 17 in a medium-exposure group, and 17 in a high-exposure group was first conducted. Then, a before–after study in the cohort of children was carried out before and two months after smoking-cessation intervention for family smokers. A total of 43 metabolites were discovered to be related to SHS exposure in children in the cross-sectional analysis (false discovery rate (FDR) corrected p < 0.05, variable importance in the projection (VIP) > 1.0). Only three metabolites were confirmed to be positively associated with children’s exposure to SHS (FDR corrected p < 0.05) in a follow-up longitudinal analysis, including kynurenine, tyrosyl-tryptophan, and 1-(3-pyridinyl)-1,4-butanediol, the latter of which belongs to carbonyl compounds, peptides, and pyridines. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that 1-(3-pyridinyl)-1,4-butanediol and kynurenine were significantly enriched in xenobiotic metabolism by cytochrome P450 (p = 0.040) and tryptophan metabolism (p = 0.030), respectively. These findings provide new insights into the pathophysiological mechanism of SHS and indicate the influence of individual differences in SHS-induced changes in urinary metabolites in children.
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12
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Quintanilla-Casas B, Marin M, Guardiola F, García-González DL, Barbieri S, Bendini A, Gallina Toschi T, Vichi S, Tres A. Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics. Foods 2020; 9:foods9101509. [PMID: 33096623 PMCID: PMC7593957 DOI: 10.3390/foods9101509] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/12/2020] [Accepted: 10/19/2020] [Indexed: 12/16/2022] Open
Abstract
The commercial category of virgin olive oil is currently assigned on the basis of chemical-physical and sensory parameters following official methods. Considering the limited number of samples that can be analysed daily by a sensory panel, an instrumental screening tool could be supportive by reducing the assessors’ workload and improving their performance. The present work aims to in-house validate a screening strategy consisting of two sequential binary partial least squares-discriminant analysis (PLS-DA) models that was suggested to be successful in a proof-of-concept study. This approach is based on the volatile fraction fingerprint obtained by HS-SPME–GC–MS from more than 300 virgin olive oils from two crop seasons graded by six different sensory panels into extra virgin, virgin or lampante categories. Uncertainty ranges were set for the binary classification models according to sensitivity and specificity by means of receiver operating characteristics (ROC) curves, aiming to identify boundary samples. Thereby, performing the screening approach, only the virgin olive oils classified as uncertain (23.3%) would be assessed by a sensory panel, while the rest would be directly classified into a given commercial category (78.9% of correct classification). The sensory panel’s workload would be reduced to less than one-third of the samples. A highly reliable classification of samples would be achieved (84.0%) by combining the proposed screening tool with the reference method (panel test) for the assessment of uncertain samples.
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Affiliation(s)
- Beatriz Quintanilla-Casas
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
| | - Marco Marin
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
| | - Francesc Guardiola
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
| | | | - Sara Barbieri
- Department of Agricultural and Food Science, Alma Mater Studiorum-Università di Bologna, 47521 Cesena, Italy; (S.B.); (A.B.); (T.G.T.)
| | - Alessandra Bendini
- Department of Agricultural and Food Science, Alma Mater Studiorum-Università di Bologna, 47521 Cesena, Italy; (S.B.); (A.B.); (T.G.T.)
| | - Tullia Gallina Toschi
- Department of Agricultural and Food Science, Alma Mater Studiorum-Università di Bologna, 47521 Cesena, Italy; (S.B.); (A.B.); (T.G.T.)
| | - Stefania Vichi
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
- Correspondence:
| | - Alba Tres
- Departament de Nutrició, Ciències de l’Alimentació i Gastronomia, Campus de l’Alimentació de Torribera, Facultat de Farmacia i Ciències de l’Alimentació, Universitat de Barcelona, 08921 Santa Coloma de Gramenet, Spain; (B.Q.-C.); (M.M.); (F.G.); (A.T.)
- Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona (UB), 08921 Santa Coloma de Gramenet, Spain
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13
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Fernández-Ochoa Á, Brunius C, Borrás-Linares I, Quirantes-Piné R, Cádiz-Gurrea MDLL, Precisesads Clinical Consortium, Alarcón Riquelme ME, Segura-Carretero A. Metabolic Disturbances in Urinary and Plasma Samples from Seven Different Systemic Autoimmune Diseases Detected by HPLC-ESI-QTOF-MS. J Proteome Res 2020; 19:3220-3229. [PMID: 32460496 DOI: 10.1021/acs.jproteome.0c00179] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Systemic autoimmune diseases (SADs) are characterized by dysfunctioning of the immune system, which causes damage in several tissues and organs. Among these pathologies are systemic lupus erythematosus (SLE), systemic sclerosis or scleroderma, Sjögren's syndrome, rheumatoid arthritis, primary antiphospholipid syndrome (PAPS), mixed connective tissue disease (MCTD), and undifferentiated connective tissue disease (UCTD). Early diagnosis is difficult due to similarity in symptoms, signs, and clinical test results. Hence, our aim was to search for differentiating metabolites of these diseases in plasma and urine samples. We performed metabolomic profiling by liquid chromatography-mass spectrometry (LC-MS) of samples from 228 SADs patients and 55 healthy volunteers. Multivariate PLS models were applied to investigate classification accuracies and identify metabolites differentiating SADs and healthy controls. Furthermore, we specifically investigated UCTD against the other SADs. PLS models were able to classify most SADs vs healthy controls (area under the roc curve (AUC) > 0.7), with the exception of MCTD and PAPS. Differentiating metabolites consisted predominantly of unsaturated fatty acids, acylglycines, acylcarnitines, and amino acids. In accordance with the difficulties in defining UCTD, the UCTD metabolome did not differentiate well from the other SADs. However, most UCTD cases were classified as SLE, suggesting that metabolomics may provide a tool to reassess UCTD diagnosis into other conditions for more well-informed therapeutic strategies.
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Affiliation(s)
- Álvaro Fernández-Ochoa
- Department of Analytical Chemistry, Faculty of Science, University of Granada, Av. Fuentenueva s/n, Granada 18071, Spain.,Research and Development of Functional Food Centre (CIDAF), Health Science Technological Park, Avda. del Conocimiento, no. 37, s/n, Granada 18016, Spain
| | - Carl Brunius
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Isabel Borrás-Linares
- Research and Development of Functional Food Centre (CIDAF), Health Science Technological Park, Avda. del Conocimiento, no. 37, s/n, Granada 18016, Spain
| | - Rosa Quirantes-Piné
- Research and Development of Functional Food Centre (CIDAF), Health Science Technological Park, Avda. del Conocimiento, no. 37, s/n, Granada 18016, Spain
| | - María de la Luz Cádiz-Gurrea
- Department of Analytical Chemistry, Faculty of Science, University of Granada, Av. Fuentenueva s/n, Granada 18071, Spain.,Research and Development of Functional Food Centre (CIDAF), Health Science Technological Park, Avda. del Conocimiento, no. 37, s/n, Granada 18016, Spain
| | | | - Marta E Alarcón Riquelme
- Centre for Genomics and Oncological Research (GENYO), Pfizer-University of Granada-Andalusian Government, Health Science Technological Park, Av. de la Ilustración 114, 18016 Granada, Spain.,Unit of Inflammatory Diseases, Institute of Environmental Medicine, Karolinska Institute, Nobels vag 13, 171 67 Solna, Sweden
| | - Antonio Segura-Carretero
- Department of Analytical Chemistry, Faculty of Science, University of Granada, Av. Fuentenueva s/n, Granada 18071, Spain.,Research and Development of Functional Food Centre (CIDAF), Health Science Technological Park, Avda. del Conocimiento, no. 37, s/n, Granada 18016, Spain
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14
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Kuhring M, Doellinger J, Nitsche A, Muth T, Renard BY. TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples. J Proteome Res 2020; 19:2501-2510. [PMID: 32362126 DOI: 10.1021/acs.jproteome.9b00714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt.
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Affiliation(s)
- Mathias Kuhring
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.,Core Unit Bioinformatics, Berlin Institute of Health (BIH), 10178 Berlin, Germany.,Berlin Institute of Health Metabolomics Platform, Berlin Institute of Health (BIH), 10178 Berlin, Germany.,Max Delbrück Center (MDC) for Molecular Medicine, 13125 Berlin, Germany
| | - Joerg Doellinger
- Centre for Biological Threats and Special Pathogens, Proteomics and Spectroscopy (ZBS 6), Robert Koch Institute, 13353 Berlin, Germany.,Centre for Biological Threats and Special Pathogens, Highly Pathogenic Viruses (ZBS 1), Robert Koch Institute, 13353 Berlin, Germany
| | - Andreas Nitsche
- Centre for Biological Threats and Special Pathogens, Highly Pathogenic Viruses (ZBS 1), Robert Koch Institute, 13353 Berlin, Germany
| | - Thilo Muth
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.,eScience Division (S.3), Federal Institute for Materials Research and Testing, 12489 Berlin, Germany
| | - Bernhard Y Renard
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany.,Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, 14482 Potsdam, Germany
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15
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Martineau E, Dumez JN, Giraudeau P. Fast quantitative 2D NMR for metabolomics and lipidomics: A tutorial. Magn Reson Chem 2020; 58:390-403. [PMID: 32239573 DOI: 10.1002/mrc.4899] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/17/2019] [Accepted: 05/28/2019] [Indexed: 06/11/2023]
Abstract
Nuclear magnetic resonance (NMR) is a well-known analytical technique for the analysis of complex mixtures. Its quantitative capability makes it ideally suited to metabolomics or lipidomics studies involving large sample collections of complex biological samples. To overcome the ubiquitous limitation of spectral overcrowding when recording 1D NMR spectra on such samples, the acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. Moreover, the experiment duration can be considerably reduced by applying fast acquisition methods. Here, we describe the general workflow to acquire fast quantitative 2D NMR spectra in the "omics" context. It is illustrated on three representative and complementary experiments: UF COSY, ZF-TOCSY with nonuniform sampling, and HSQC with nonuniform sampling. After giving some details and recommendations on how to apply this protocol, its implementation in the case of targeted and untargeted metabolomics/lipidomics studies is described.
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Affiliation(s)
- Estelle Martineau
- CEISAM, CNRS UMR 6230, Université de Nantes, Nantes, France
- SpectroMaitrise, CAPACITES SAS, Nantes, France
| | | | - Patrick Giraudeau
- CEISAM, CNRS UMR 6230, Université de Nantes, Nantes, France
- Institut Universitaire de France, Paris Cedex 5, France
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16
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Abstract
Metabolomics has grown into one of the major approaches for systems biology studies, in part driven by developments in mass spectrometry (MS), providing high sensitivity and coverage of the metabolome at high throughput. Untargeted metabolomics allows for the investigation of metabolic phenotypes involving several hundreds to thousands of metabolites. In this approach, all signals in a mass chromatogram are processed in an unbiased way, allowing for a deeper investigation of metabolic phenotypes, but also resulting in significantly more complex data processing and post-processing steps. In this article, we discuss all the intricacies involved in extracting and analyzing metabolites by chromatography coupled to MS, as well as the processing and analysis of such datasets. © 2019 The Authors. Basic Protocol 1: Metabolite extraction for LC-MS Alternate Protocol: Methyl tert-butyl ether (MTBE) extraction for multiple mass spectrometry platforms (GC-polar, LC-polar, LC-lipid) Basic Protocol 2: LC-MS analysis Support Protocol 1: GC-MS derivatization and analysis Support Protocol 2: Lipid analysis Basic Protocol 3: LC-MS data processing Basic Protocol 4: Data analysis Basic Protocol 5: Metabolite annotation Support Protocol 3: Molecular networking using MetNet Support Protocol 4: Co-injection of authentic standards.
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Affiliation(s)
| | - Saleh Alseekh
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.,Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Alisdair Fernie
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.,Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
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17
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Vázquez-Manjarrez N, Weinert CH, Ulaszewska MM, Mack CI, Micheau P, Pétéra M, Durand S, Pujos-Guillot E, Egert B, Mattivi F, Bub A, Dragsted LO, Kulling SE, Manach C. Discovery and Validation of Banana Intake Biomarkers Using Untargeted Metabolomics in Human Intervention and Cross-sectional Studies. J Nutr 2019; 149:1701-1713. [PMID: 31240312 DOI: 10.1093/jn/nxz125] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 04/17/2019] [Accepted: 05/14/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Banana is one of the most widely consumed fruits in the world. However, information regarding its health effects is scarce. Biomarkers of banana intake would allow a more accurate assessment of its consumption in nutrition studies. OBJECTIVES Using an untargeted metabolomics approach, we aimed to identify the banana-derived metabolites present in urine after consumption, including new candidate biomarkers of banana intake. METHODS A randomized controlled study with a crossover design was performed on 12 healthy subjects (6 men, 6 women, mean ± SD age: 30.0 ± 4.9 y; mean ± SD BMI: 22.5 ± 2.3 kg/m2). Subjects underwent 2 dietary interventions: 1) 250 mL control drink (Fresubin 2 kcal fiber, neutral flavor; Fresenius Kabi), and 2) 240 g banana + 150 mL control drink. Twenty-four-hour urine samples were collected and analyzed with ultra-performance liquid chromatography coupled to a quadrupole time-of-flight MS and 2-dimensional GC-MS. The discovered biomarkers were confirmed in a cross-sectional study [KarMeN (Karlsruhe Metabolomics and Nutrition study)] in which 78 subjects (mean BMI: 22.8; mean age: 47 y) were selected reflecting high intake (126-378 g/d), low intake (47.3-94.5 g/d), and nonconsumption of banana. The confirmed biomarkers were examined singly or in combinations, for established criteria of validation for biomarkers of food intake. RESULTS We identified 33 potentially bioactive banana metabolites, of which 5 metabolites, methoxyeugenol glucuronide (MEUG-GLUC), dopamine sulfate (DOP-S), salsolinol sulfate, xanthurenic acid, and 6-hydroxy-1-methyl-1,2,3,4-tetrahydro-β-carboline sulfate, were confirmed as candidate intake biomarkers. We demonstrated that the combination of MEUG-GLUC and DOP-S performed best in predicting banana intake in high (AUCtest = 0.92) and low (AUCtest = 0.87) consumers. The new biomarkers met key criteria establishing their current applicability in nutrition and health research for assessing the occurrence of banana intake. CONCLUSIONS Our metabolomics study in healthy men and women revealed new putative bioactive metabolites of banana and a combined biomarker of intake. These findings will help to better decipher the health effects of banana in future focused studies. This study was registered at clinicaltrials.gov as NCT03581955 and with the Ethical Committee for the Protection of Human Subjects Sud-Est 6 as CPP AU 1251, IDRCB 2016-A0013-48; the KarMeN study was registered with the German Clinical Trials Register (DRKS00004890). Details about the study can be obtained from https://www.drks.de.
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Affiliation(s)
- Natalia Vázquez-Manjarrez
- Human Nutrition Unit, INRA, Université Clermont Auvergne, Clermont-Ferrand, France.,Department of Nutrition, Exercise, and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Christoph H Weinert
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Maria M Ulaszewska
- Department of Food Quality and Nutrition, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy
| | - Carina I Mack
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Pierre Micheau
- Human Nutrition Unit, INRA, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Mélanie Pétéra
- Human Nutrition Unit, Plateforme d'Exploration du Métabolisme MetaboHUB, INRA, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Stephanie Durand
- Human Nutrition Unit, Plateforme d'Exploration du Métabolisme MetaboHUB, INRA, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Estelle Pujos-Guillot
- Human Nutrition Unit, Plateforme d'Exploration du Métabolisme MetaboHUB, INRA, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Björn Egert
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Fondazione Edmund Mach, Research and Innovation Centre, San Michele all'Adige, Italy.,Centre of Agriculture Food Environment, University of Trento, San Michele all'Adige, Italy
| | - Achim Bub
- Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany
| | - Lars Ove Dragsted
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Sabine E Kulling
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Karlsruhe, Germany
| | - Claudine Manach
- Human Nutrition Unit, INRA, Université Clermont Auvergne, Clermont-Ferrand, France
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18
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Abstract
Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture-based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.
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Affiliation(s)
- Ashish Kumar
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, 78227, USA
| | - Biswapriya B Misra
- Center for Precision Medicine, Department of Internal Medicine, Section on Molecular Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA
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19
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Forest A, Ruiz M, Bouchard B, Boucher G, Gingras O, Daneault C, Frayne IR, Rhainds D, Tardif JC, Rioux JD, Rosiers CD. Comprehensive and Reproducible Untargeted Lipidomic Workflow Using LC-QTOF Validated for Human Plasma Analysis. J Proteome Res 2018; 17:3657-3670. [PMID: 30256116 PMCID: PMC6572761 DOI: 10.1021/acs.jproteome.8b00270] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The goal of this work was to develop a label-free, comprehensive, and reproducible high-resolution liquid chromatography-mass spectrometry (LC-MS)-based untargeted lipidomic workflow using a single instrument, which could be applied to biomarker discovery in both basic and clinical studies. For this, we have (i) optimized lipid extraction and elution to enhance coverage of polar and nonpolar lipids as well as resolution of their isomers, (ii) ensured MS signal reproducibility and linearity, and (iii) developed a bioinformatic pipeline to correct remaining biases. Workflow validation is reported for 48 replicates of a single human plasma sample: 1124 reproducible LC-MS signals were extracted (median signal intensity RSD = 10%), 50% of which are redundant due to adducts, dimers, in-source fragmentation, contaminations, or positive and negative ion duplicates. From the resulting 578 unique compounds, 428 lipids were identified by MS/MS, including acyl chain composition, of which 394 had RSD < 30% inside their linear intensity range, thereby enabling robust semiquantitation. MS signal intensity spanned 4 orders of magnitude, covering 16 lipid subclasses. Finally, the power of our workflow is illustrated by a proof-of-concept study in which 100 samples from healthy human subjects were analyzed and the data set was investigated using three different statistical testing strategies in order to compare their capacity in identifying the impact of sex and age on circulating lipids.
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Affiliation(s)
- Anik Forest
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | - Matthieu Ruiz
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
- Department of Medicine, Université de
Montréal, Montreal, Quebec, Canada
| | - Bertrand Bouchard
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | - Gabrielle Boucher
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | - Olivier Gingras
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | - Caroline Daneault
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | | | - David Rhainds
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
| | | | | | - Jean-Claude Tardif
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
- Department of Medicine, Université de
Montréal, Montreal, Quebec, Canada
| | - John D. Rioux
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
- Department of Medicine, Université de
Montréal, Montreal, Quebec, Canada
| | - Christine Des Rosiers
- Montreal Heart Institute, Research Center, 5000
Belanger Street, Montreal, Quebec, Canada H1T 1C8
- Department of Nutrition, Université de
Montréal, Montreal, Quebec, Canada
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20
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Rochat B, Mohamed R, Sottas PE. LC-HRMS Metabolomics for Untargeted Diagnostic Screening in Clinical Laboratories: A Feasibility Study. Metabolites 2018; 8:metabo8020039. [PMID: 29914076 PMCID: PMC6027396 DOI: 10.3390/metabo8020039] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 06/11/2018] [Accepted: 06/13/2018] [Indexed: 11/25/2022] Open
Abstract
Today’s high-resolution mass spectrometers (HRMS) allow bioanalysts to perform untargeted/global determinations that can reveal unexpected compounds or concentrations in a patient’s sample. This could be performed for preliminary diagnosis attempts when usual diagnostic processes and targeted determinations fail. We have evaluated an untargeted diagnostic screening (UDS) procedure. UDS is a metabolome analysis that compares one sample (e.g., a patient) with control samples (a healthy population). Using liquid chromatography (LC)-HRMS full-scan analysis of human serum extracts and unsupervised data treatment, we have compared individual samples that were spiked with one xenobiotic or a higher level of one endogenous compound with control samples. After the use of different filters that drastically reduced the number of metabolites detected, the spiked compound was eventually revealed in each test sample and ranked. The proposed UDS procedure appears feasible and reliable to reveal unexpected xenobiotics (toxicology) or higher concentrations of endogenous metabolites. HRMS-based untargeted approaches could be useful as preliminary diagnostic screening when canonical processes do not reveal disease etiology nor establish a clear diagnosis and could reduce misdiagnosis. On the other hand, the risk of overdiagnosis of this approach should be reduced with mandatory biomedical interpretation of the patient’s UDS results and with confirmatory targeted and quantitative determinations.
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Affiliation(s)
- Bertrand Rochat
- Protein Analysis Facility, Center for Integrative Genomics (CIG), University of Lausanne, CH-1015 Lausanne, Switzerland.
| | - Rayane Mohamed
- Département Formation Recherche, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1011 Lausanne, Switzerland.
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21
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Bagheri M, Farzadfar F, Qi L, Yekaninejad MS, Chamari M, Zeleznik OA, Kalantar Z, Ebrahimi Z, Sheidaie A, Koletzko B, Uhl O, Djazayery A. Obesity-Related Metabolomic Profiles and Discrimination of Metabolically Unhealthy Obesity. J Proteome Res 2018; 17:1452-1462. [PMID: 29493238 DOI: 10.1021/acs.jproteome.7b00802] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
A particular subgroup of obese adults, considered as metabolically healthy obese (MHO), has a reduced risk of metabolic complications. However, the molecular basis contributing to this healthy phenotype remains unclear. The objective of this work was to identify obesity-related metabolite patterns differed between MHO and metabolically unhealthy obese (MUHO) groups and examine whether these patterns are associated with the development of cardiometabolic disorders in a sample of Iranian adult population aged 18-50 years. Valid metabolites were defined as metabolites that passed the quality control analysis of the study. In this case-control study, 104 valid metabolites of 107 MHO and 100 MUHO patients were separately compared to those of 78 normal-weight metabolically healthy (NWMH) adults. Multivariable linear regression was used to investigate all potential relations in the study. A targeted metabolomic approach using liquid chromatography coupled to triple quadrupole mass spectrometry was employed to profile plasma metabolites. The study revealed that, after Bonferroni correction, branched-chain amino-acids, tyrosine, glutamic acid, diacyl-phosphatidylcholines C32:1 and C38:3 were directly and acyl-carnitine C18:2, acyl-lysophosphatidylcholines C18:1 and C18:2, and alkyl-lysophosphatidylcholines C18.0 were inversely associated with MHO phenotype. The same patterns were observed in MUHO patients except for the acyl-carnitine and lysophosphatidylcholine profiles where acyl-carnitine C3:0 and acyl-lysophosphatidylcholine C16:1 were higher and acyl-lysophosphatidylcholines C18:1, C18:2 were lower in this phenotype. Furthermore, proline, and diacyl-phosphatidylcholines C32:2 and C34:2 were directly and serine, asparagines, and acyl-alkyl-phosphatidylcholine C34:3 were negatively linked to MUHO group. Factors composed of amino acids were directly and those containing lysophosphatidylcholines were inversely related to cardiometabolic biomarkers in both phenotypes. Interestingly, the diacyl-phosphatidylcholines-containing factor was directly associated with cardiometabolic disorders in the MUHO group. A particular pattern of amino acids and choline-containing phospholipids may aid in the identification of metabolic health among obese patients.
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Affiliation(s)
- Minoo Bagheri
- Department of Community Nutrition, School of Nutritional Sciences and Dietetic, Tehran University of Medical Sciences, Tehran 1416-643931, Iran
| | - Farshad Farzadfar
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran 1416-643931, Iran
| | - Lu Qi
- Department of Nutrition , Harvard T.H. Chan School of Public Health , Boston , Massachusetts 02115 , United States
| | - Mir Saeed Yekaninejad
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 1416-643931, Iran
| | - Maryam Chamari
- Department of Community Nutrition, School of Nutritional Sciences and Dietetic, Tehran University of Medical Sciences, Tehran 1416-643931, Iran
| | - Oana A Zeleznik
- Channing Division of Network Medicine , Harvard Medical School and Brigham and Women's Hospital , Boston , Massachusetts 02115 , United States
| | - Zahra Kalantar
- Department of Cellular and Molecular Nutrition School of Nutritional Sciences and Dietetic, Tehran University of Medical Sciences, Tehran 1416-643931, Iran
| | - Zarin Ebrahimi
- Department of Nutrition, Faculty of Health , Shahid Sadoughi University of Medical Sciences , Yazd 8916-877391 , Iran
| | - Ali Sheidaie
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran 1416-643931, Iran
| | - Berthold Koletzko
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital , Ludwig-Maximilians-Universität München , 80337 Munich , Germany
| | - Olaf Uhl
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital , Ludwig-Maximilians-Universität München , 80337 Munich , Germany
| | - Abolghasem Djazayery
- Department of Community Nutrition, School of Nutritional Sciences and Dietetic, Tehran University of Medical Sciences, Tehran 1416-643931, Iran
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22
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Ronningen I, Miller M, Xia Y, Peterson DG. Identification and Validation of Sensory-Active Compounds from Data-Driven Research: A Flavoromics Approach. J Agric Food Chem 2018; 66:2473-2479. [PMID: 28525713 DOI: 10.1021/acs.jafc.7b00093] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this study, highly predictive LC-MS features (retention time_ m/ z) derived from untargeted chemical fingerprinting-multivariate analysis (MVA) previously used to model flavor changes in citrus fruits related to aging (freshness) were further isolated and analyzed for sensory impact, followed by structural elucidation. The top 10 statistical features from two MVA approaches, partial least-squares data analysis (PLS-DA) and Random Forrest (RF), were purified to approximately 70% via multidimensional liquid chromatography-mass-directed fractionation to screen for sensory activity. When added to a 'fresh' orange flavor model system, 50-60% of the isolates were reported to cause a sensory change. From the subset of the actives identified, two compounds were selected, on the basis of statistical relevance, that were further purified to >97% for identification (MS, NMR) and for sensory descriptive analysis (DA). The compounds were identified as nomilin glucoside and a novel ionone glucoside. DA evaluation in the recombination orange model indicated both compounds statistically suppressed the perceived intensity of the "orange character" attribute, whereas the novel ionone glycoside also decreased the intensity of the floral character while increasing the green bean attribute intensity.
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Affiliation(s)
- Ian Ronningen
- Department of Food Science , University of Minnesota , St. Paul , Minnesota 55108 , United States
| | - Michelle Miller
- MNMR Center , University of Minnesota , Minneapolis , Minnesota 55455 , United States
| | - Youlin Xia
- MNMR Center , University of Minnesota , Minneapolis , Minnesota 55455 , United States
| | - Devin G Peterson
- Department of Food Science , University of Minnesota , St. Paul , Minnesota 55108 , United States
- 317 Parker Building, Food Science & Technology , The Ohio State University , 2015 Fyffe Road , Columbus , Ohio 43210 , United States
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23
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Abstract
BACKGROUND Second generation antipsychotic (SGA) use in bipolar disorder is common and has proven effective in short-term trials. There continues to be a lack of understanding of the mechanisms underlying many of their positive and negative effects in bipolar disorder. This study aimed to describe the metabolite profiles of bipolar subjects treated with SGAs by comparing to metabolite profiles of bipolar subjects treated with lithium, and schizophrenia subjects treated with SGAs. METHODS Cross-sectional, fasting untargeted serum metabolomic profiling was conducted in 82 subjects diagnosed with bipolar I disorder (n = 30 on SGAs and n = 32 on lithium) or schizophrenia (n = 20). Metabolomic profiles of bipolar subjects treated with SGAs were compared to bipolar subjects treated with lithium and schizophrenia subjects treated with SGAs using multivariate methods. RESULTS Partial lease square discriminant analysis (PLS-DA) plots showed separation between bipolar subjects treated with SGAs, bipolar subjects treated with lithium, or schizophrenia subjects treated with SGAs. Top influential metabolite features were associated with several pathways including that of polyunsaturated fatty acids, pyruvate, glucose, and branched chain amino acids. CONCLUSIONS The findings from this study require further validation in pre- and posttreated bipolar and schizophrenia subjects, but suggest that the pharmacometabolome may be diagnosis specific.
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Affiliation(s)
- Kyle J Burghardt
- Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, Michigan, USA
| | - Simon J Evans
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
| | - Kristen M Wiese
- College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA
| | - Vicki L Ellingrod
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.,College of Pharmacy, University of Michigan, Ann Arbor, Michigan, USA
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Abstract
Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.
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Affiliation(s)
- Arnald Alonso
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
- Department of Automatic Control (ESAII), Polytechnic University of Catalonia, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain
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25
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Perng W, Gillman MW, Fleisch AF, Michalek RD, Watkins SM, Isganaitis E, Patti ME, Oken E. Metabolomic profiles and childhood obesity. Obesity (Silver Spring) 2014; 22:2570-8. [PMID: 25251340 PMCID: PMC4236243 DOI: 10.1002/oby.20901] [Citation(s) in RCA: 127] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Accepted: 08/21/2014] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To identify metabolite patterns associated with childhood obesity, to examine relations of these patterns with measures of adiposity and cardiometabolic risk, and to evaluate associations with maternal peripartum characteristics. METHODS Untargeted metabolomic profiling was used to quantify metabolites in plasma of 262 children (6-10 years). Principal components analysis was used to consolidate 345 metabolites into 18 factors and identified two that differed between obese (BMI ≥ 95‰; n = 84) and lean children (BMI < 85‰; n = 150). The relations of these factors with adiposity (fat mass, BMI, skinfold thicknesses) and cardiometabolic biomarkers (HOMA-IR, triglycerides, leptin, adiponectin, hsCRP, IL-6) using multivariable linear regression was then investigated. Finally, the associations of maternal prepregnancy obesity, gestational weight gain, and gestational glucose tolerance with the offspring metabolite patterns was examined. RESULTS A branched-chain amino acid (BCAA)-related pattern and an androgen hormone pattern were higher in obese vs. lean children. Both patterns were associated with adiposity and worse cardiometabolic profiles. For example, each increment in the BCAA and androgen pattern scores corresponded with 6% (95% CI: 1, 13%) higher HOMA-IR. Children of obese mothers had 0.61 (0.13, 1.08) higher BCAA score than their counterparts. CONCLUSIONS BCAA and androgen metabolites were associated with adiposity and cardiometabolic risk during mid-childhood. Maternal obesity may contribute to altered offspring BCAA metabolism.
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Affiliation(s)
- Wei Perng
- Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Matthew W. Gillman
- Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Abby F. Fleisch
- Endocrinology Division, Boston Children’s Hospital, Boston, MA, USA
| | | | | | | | | | - Emily Oken
- Obesity Prevention Program, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
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