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Zulfiqar M, Stettin D, Schmidt S, Nikitashina V, Pohnert G, Steinbeck C, Peters K, Sorokina M. Untargeted metabolomics to expand the chemical space of the marine diatom Skeletonema marinoi. Front Microbiol 2023; 14:1295994. [PMID: 38116530 PMCID: PMC10728474 DOI: 10.3389/fmicb.2023.1295994] [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: 09/17/2023] [Accepted: 10/31/2023] [Indexed: 12/21/2023] Open
Abstract
Diatoms (Bacillariophyceae) are aquatic photosynthetic microalgae with an ecological role as primary producers in the aquatic food web. They account substantially for global carbon, nitrogen, and silicon cycling. Elucidating the chemical space of diatoms is crucial to understanding their physiology and ecology. To expand the known chemical space of a cosmopolitan marine diatom, Skeletonema marinoi, we performed High-Resolution Liquid Chromatography-Tandem Mass Spectrometry (LC-MS2) for untargeted metabolomics data acquisition. The spectral data from LC-MS2 was used as input for the Metabolome Annotation Workflow (MAW) to obtain putative annotations for all measured features. A suspect list of metabolites previously identified in the Skeletonema spp. was generated to verify the results. These known metabolites were then added to the putative candidate list from LC-MS2 data to represent an expanded catalog of 1970 metabolites estimated to be produced by S. marinoi. The most prevalent chemical superclasses, based on the ChemONT ontology in this expanded dataset, were organic acids and derivatives, organoheterocyclic compounds, lipids and lipid-like molecules, and organic oxygen compounds. The metabolic profile from this study can aid the bioprospecting of marine microalgae for medicine, biofuel production, agriculture, and environmental conservation. The proposed analysis can be applicable for assessing the chemical space of other microalgae, which can also provide molecular insights into the interaction between marine organisms and their role in the functioning of ecosystems.
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Affiliation(s)
- Mahnoor Zulfiqar
- Faculty of Chemistry and Earth Sciences, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Daniel Stettin
- Faculty of Chemistry and Earth Sciences, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Jena, Germany
| | - Saskia Schmidt
- Faculty of Chemistry and Earth Sciences, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Jena, Germany
| | - Vera Nikitashina
- Faculty of Chemistry and Earth Sciences, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Jena, Germany
| | - Georg Pohnert
- Faculty of Chemistry and Earth Sciences, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Christoph Steinbeck
- Faculty of Chemistry and Earth Sciences, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Kristian Peters
- iDiv - German Centre for Integrative Biodiversity Research, Halle-Jena-Leipzig, Leipzig, Germany
- Geobotany and Botanical Gardens, Martin-Luther University of Halle-Wittenberg, Halle, Germany
- Institute of Plant Biochemistry, Leibniz Institute of Plant Biochemistry, Halle, Germany
| | - Maria Sorokina
- Faculty of Chemistry and Earth Sciences, Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Jena, Germany
- Pharmaceuticals Division, Research & Development, Data Science and Artificial Intelligence, AG Bayer, Berlin, Germany
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2
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Lu W, Park NR, TeSlaa T, Jankowski CS, Samarah L, McReynolds M, Xing X, Schembri J, Woolf MT, Rabinowitz JD, Davidson SM. Acidic Methanol Treatment Facilitates Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry Imaging of Energy Metabolism. Anal Chem 2023; 95:14879-14888. [PMID: 37756255 PMCID: PMC10568533 DOI: 10.1021/acs.analchem.3c01875] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 08/15/2023] [Indexed: 09/29/2023]
Abstract
Detection of small molecule metabolites (SMM), particularly those involved in energy metabolism using MALDI-mass spectrometry imaging (MSI), is challenging due to factors including ion suppression from other analytes present (e.g., proteins and lipids). One potential solution to enhance SMM detection is to remove analytes that cause ion suppression from tissue sections before matrix deposition through solvent washes. Here, we systematically investigated solvent treatment conditions to improve SMM signal and preserve metabolite localization. Washing with acidic methanol significantly enhances the detection of phosphate-containing metabolites involved in energy metabolism. The improved detection is due to removing lipids and highly polar metabolites that cause ion suppression and denaturing proteins that release bound phosphate-containing metabolites. Stable isotope infusions of [13C6]nicotinamide coupled to MALDI-MSI ("Iso-imaging") in the kidney reveal patterns that indicate blood vessels, medulla, outer stripe, and cortex. We also observed different ATP:ADP raw signals across mouse kidney regions, consistent with regional differences in glucose metabolism favoring either gluconeogenesis or glycolysis. In mouse muscle, Iso-imaging using [13C6]glucose shows high glycolytic flux from infused circulating glucose in type 1 and 2a fibers (soleus) and relatively lower glycolytic flux in type 2b fiber type (gastrocnemius). Thus, improved detection of phosphate-containing metabolites due to acidic methanol treatment combined with isotope tracing provides an improved way to probe energy metabolism with spatial resolution in vivo.
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Affiliation(s)
- Wenyun Lu
- Lewis
Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, United States
- Department
of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Noel R. Park
- Lewis
Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, United States
| | - Tara TeSlaa
- Department
of Molecular and Medical Pharmacology, University
of California Los Angeles, Los Angeles, California 90095, United States
| | - Connor S.R. Jankowski
- Lewis
Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, United States
| | - Laith Samarah
- Lewis
Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, United States
| | - Melanie McReynolds
- Department
of Biochemistry and Molecular Biology, The
Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Xi Xing
- Lewis
Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, United States
- Department
of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Jessica Schembri
- Lewis
Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, United States
| | - Morgan T. Woolf
- Department
of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Joshua D. Rabinowitz
- Lewis
Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, United States
- Rutgers
Cancer Institute of New Jersey (CINJ), Rutgers
University, New Brunswick, New Jersey 08901, United States
- Department
of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
- Ludwig
Institute for Cancer Research, Princeton
University, Princeton, New Jersey 08544, United States
| | - Shawn M. Davidson
- Lewis
Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, United States
- Rutgers
Cancer Institute of New Jersey (CINJ), Rutgers
University, New Brunswick, New Jersey 08901, United States
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3
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Quintás G, Castell JV, Moreno-Torres M. The assessment of the potential hepatotoxicity of new drugs by in vitro metabolomics. Front Pharmacol 2023; 14:1155271. [PMID: 37214440 PMCID: PMC10196061 DOI: 10.3389/fphar.2023.1155271] [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: 01/31/2023] [Accepted: 04/19/2023] [Indexed: 05/24/2023] Open
Abstract
Drug hepatotoxicity assessment is a relevant issue both in the course of drug development as well as in the post marketing phase. The use of human relevant in vitro models in combination with powerful analytical methods (metabolomic analysis) is a promising approach to anticipate, as well as to understand and investigate the effects and mechanisms of drug hepatotoxicity in man. The metabolic profile analysis of biological liver models treated with hepatotoxins, as compared to that of those treated with non-hepatotoxic compounds, provides useful information for identifying disturbed cellular metabolic reactions, pathways, and networks. This can later be used to anticipate, as well to assess, the potential hepatotoxicity of new compounds. However, the applicability of the metabolomic analysis to assess the hepatotoxicity of drugs is complex and requires careful and systematic work, precise controls, wise data preprocessing and appropriate biological interpretation to make meaningful interpretations and/or predictions of drug hepatotoxicity. This review provides an updated look at recent in vitro studies which used principally mass spectrometry-based metabolomics to evaluate the hepatotoxicity of drugs. It also analyzes the principal drawbacks that still limit its general applicability in safety assessment screenings. We discuss the analytical workflow, essential factors that need to be considered and suggestions to overcome these drawbacks, as well as recent advancements made in this rapidly growing field of research.
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Affiliation(s)
- Guillermo Quintás
- Metabolomics and Bioanalysis, Health and Biomedicine, Leitat Technological Center, Barcelona, Spain
- Analytical Unit, Health Research Institute La Fe, Valencia, Spain
| | - José V. Castell
- Unidad Mixta de Hepatología Experimental, Instituto de Investigación Sanitaria del Hospital La Fe (IIS La Fe), Valencia, Spain
- Departamento de Bioquímica y Biología Molecular, Facultad de Medicina, Universidad de Valencia, Valencia, Spain
- CIBEREHD, Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Moreno-Torres
- Unidad Mixta de Hepatología Experimental, Instituto de Investigación Sanitaria del Hospital La Fe (IIS La Fe), Valencia, Spain
- Departamento de Bioquímica y Biología Molecular, Facultad de Medicina, Universidad de Valencia, Valencia, Spain
- CIBEREHD, Instituto de Salud Carlos III, Madrid, Spain
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4
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Stepanovic S, Hopfgartner G. Predicting Preferences for Adduct Formation in Electrospray Ionization: The Case Study of Succinic Acid. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:562-569. [PMID: 36944084 DOI: 10.1021/jasms.2c00297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A simple theoretical approach is developed that can be used to predict the preference of ion adduct formation (with alkali Li+, Na+, K+ and alkaline earth Ca2+, Mg2+ metals) in electrospray ionization mass spectrometry (ESI-MS) of succinic acid, associated with several protonation/deprotonation equilibria. The applied strategy consists of using a vacuum environment as well as both implicit and explicit solvation of reactive sites and density functional theory as the method of choice. These distinct levels of theory mimic the smooth transition between the condensed environment and free ion in the gas phase. Good correlation between the Gibbs free energies for protonation/adduct formation processes with peak observation in the obtained mass spectra provide insight into the physical basis behind adduct preference and selectivity. This signifies the relationship between microscopic interactions, ionization efficiency, and types of ions that reach the detector.
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Affiliation(s)
- Stepan Stepanovic
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, CH-1211 Geneva 4 Switzerland
- Institute of Chemistry, Technology and Metallurgy, University of Belgrade, Njegoševa 12, 11000 Belgrade, Serbia
| | - Gérard Hopfgartner
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, CH-1211 Geneva 4 Switzerland
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5
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Yu D, Zhou L, Liu X, Xu G. Stable isotope-resolved metabolomics based on mass spectrometry: Methods and their applications. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
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6
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Label-free and dual-mode biosensor for HPV DNA based on DNA/silver nanoclusters and G-quadruplex/hemin DNAzyme. Talanta 2022; 247:123554. [DOI: 10.1016/j.talanta.2022.123554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 12/15/2022]
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7
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Zhu Y, Zang Q, Luo Z, He J, Zhang R, Abliz Z. An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body. Anal Chem 2022; 94:7286-7294. [PMID: 35548855 DOI: 10.1021/acs.analchem.2c00557] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC-MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.
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Affiliation(s)
- Ying Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Qingce Zang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zhigang Luo
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Ruiping Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zeper Abliz
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China.,Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), National Ethnic Affairs Commission, Beijing 100081, China.,Center for Imaging and Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
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8
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Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
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9
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Griffith CM, Walvekar AS, Linster CL. Approaches for completing metabolic networks through metabolite damage and repair discovery. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 28:None. [PMID: 34957344 PMCID: PMC8669784 DOI: 10.1016/j.coisb.2021.100379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Metabolites are prone to damage, either via enzymatic side reactions, which collectively form the underground metabolism, or via spontaneous chemical reactions. The resulting non-canonical metabolites that can be toxic, are mended by dedicated "metabolite repair enzymes." Deficiencies in the latter can cause severe disease in humans, whereas inclusion of repair enzymes in metabolically engineered systems can improve the production yield of value-added chemicals. The metabolite damage and repair loops are typically not yet included in metabolic reconstructions and it is likely that many remain to be discovered. Here, we review strategies and associated challenges for unveiling non-canonical metabolites and metabolite repair enzymes, including systematic approaches based on high-resolution mass spectrometry, metabolome-wide side-activity prediction, as well as high-throughput substrate and phenotypic screens.
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Affiliation(s)
| | | | - Carole L. Linster
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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10
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Cerrato A, Aita SE, Capriotti AL, Cavaliere C, Montone CM, Piovesana S, Laganà A. Fully Automatized Detection of Phosphocholine-Containing Lipids through an Isotopically Labeled Buffer Modification Workflow. Anal Chem 2021; 93:15042-15048. [PMID: 34726396 DOI: 10.1021/acs.analchem.1c02944] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
High-resolution mass spectrometry is the foremost technique for qualitative and quantitative lipidomics analyses. Glycerophospholipids and sphingolipids, collectively termed polar lipids, are commonly investigated by hyphenated liquid chromatography-mass spectrometry (LC-MS) techniques that reduce aggregation effects and provide a greater dynamic range of detection sensitivity compared to shotgun lipidomics. However, automatic polar lipid identification is hindered by several isobaric and isomer mass overlaps, which cause software programs to often fail to correctly annotate the lipid species. In the present paper, a buffer modification workflow based on the use of labeled and unlabeled acetate ions in the chromatographic buffers was optimized by Box-Behnken design of the experiments and applied to the characterization of phosphocholine-containing lipids in human plasma samples. The contemporary generation of [M + CH3COO]-, [M + CD3COO]-, and [M - CH3]- coupled with a dedicated data processing workflow, which was specifically set up on Compound Discoverer software, allowed us to correctly determine adduct composition, molecular formulas, and grouping, as well as granting a lower false-positive rate and streamlining the manual validation step compared to commonly employed lipidomics platforms. The proposed workflow represents a robust yet easier alternative to the existing approaches for improving lipid annotation, as it does not require extensive sample pretreatment or prior isotopic enrichment or derivatization.
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Affiliation(s)
- Andrea Cerrato
- Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Sara Elsa Aita
- Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Anna Laura Capriotti
- Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Chiara Cavaliere
- Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Carmela Maria Montone
- Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Susy Piovesana
- Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Aldo Laganà
- Department of Chemistry, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.,CNR NANOTEC, Campus Ecotekne, University of Salento, Via Monteroni, 73100 Lecce, Italy
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11
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Chen L, Lu W, Wang L, Xing X, Chen Z, Teng X, Zeng X, Muscarella AD, Shen Y, Cowan A, McReynolds MR, Kennedy BJ, Lato AM, Campagna SR, Singh M, Rabinowitz JD. Metabolite discovery through global annotation of untargeted metabolomics data. Nat Methods 2021; 18:1377-1385. [PMID: 34711973 PMCID: PMC8733904 DOI: 10.1038/s41592-021-01303-3] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 09/16/2021] [Indexed: 11/08/2022]
Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak-peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
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Affiliation(s)
- Li Chen
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Wenyun Lu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Lin Wang
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Xi Xing
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Ziyang Chen
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Xin Teng
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Xianfeng Zeng
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Antonio D Muscarella
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Yihui Shen
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Alexis Cowan
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Melanie R McReynolds
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Brandon J Kennedy
- Lotus Separations, LLC, Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Ashley M Lato
- Department of Chemistry, The University of Tennessee at Knoxville, Knoxville, TN, USA
| | - Shawn R Campagna
- Department of Chemistry, The University of Tennessee at Knoxville, Knoxville, TN, USA
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Joshua D Rabinowitz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
- Department of Chemistry, Princeton University, Princeton, NJ, USA.
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA.
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12
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Abstract
BACKGROUND Precision medicine, space exploration, drug discovery to characterization of dark chemical space of habitats and organisms, metabolomics takes a centre stage in providing answers to diverse biological, biomedical, and environmental questions. With technological advances in mass-spectrometry and spectroscopy platforms that aid in generation of information rich datasets that are complex big-data, data analytics tend to co-evolve to match the pace of analytical instrumentation. Software tools, resources, databases, and solutions help in harnessing the concealed information in the generated data for eventual translational success. AIM OF THE REVIEW In this review, ~ 85 metabolomics software resources, packages, tools, databases, and other utilities that appeared in 2020 are introduced to the research community. KEY SCIENTIFIC CONCEPTS OF REVIEW In Table 1 the computational dependencies and downloadable links of the tools are provided, and the resources are categorized based on their utility. The review aims to keep the community of metabolomics researchers updated with all the resources developed in 2020 at a collated avenue, in line with efforts form 2015 onwards to help them find these at one place for further referencing and use.
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13
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Abstract
Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.
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Affiliation(s)
- Mark C. Blaser
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon Kraler
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
| | - Thomas F. Lüscher
- Center for Molecular Cardiology, University of Zurich, Schlieren, CH
- Heart Division, Royal Brompton & Harefield Hospitals, London, UK
- National Heart and Lung Institute, Imperial College, London, UK
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Excellence in Vascular Biology, Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021; 93:519-545. [PMID: 33249827 PMCID: PMC7807424 DOI: 10.1021/acs.analchem.0c04698] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Evelyn Rampler
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Yasin El Abiead
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Harald Schoeny
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Mate Rusz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Institute of Inorganic
Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, Austria
| | - Felina Hildebrand
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Veronika Fitz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
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