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Dutertre Q, Guy PA, Sutour S, Peitsch MC, Ivanov NV, Glauser G, von Reuss S. Identification of Granatane Alkaloids from Duboisia myoporoides (Solanaceae) using Molecular Networking and Semisynthesis. JOURNAL OF NATURAL PRODUCTS 2024; 87:1914-1920. [PMID: 39038492 PMCID: PMC11348422 DOI: 10.1021/acs.jnatprod.4c00304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 07/05/2024] [Accepted: 07/05/2024] [Indexed: 07/24/2024]
Abstract
The Solanaceae plant family contains at least 98 genera and over 2700 species. The Duboisia genus stands out for its ability to produce pyridine and tropane alkaloids, which are relatively poorly characterized at the phytochemical level. In this study, we analyzed dried leaves of Duboisia spp. using supercritical CO2 extraction and ultra-high-pressure liquid chromatography coupled to high-resolution tandem mass spectrometry, followed by feature-based molecular networking. Thirty-one known tropane alkaloids were putatively annotated, and the identity of six (atropine, scopolamine, anisodamine, aposcopolamine, apoatropine, and noratropine) were identified using reference standards. Two new granatane alkaloids connected in the molecular network were highlighted from Duboisia myoporoides, and their α-granatane tropate and α-granatane isovalerate structures were unambiguously established by semisynthesis.
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Affiliation(s)
- Quentin Dutertre
- Philip
Morris Product SA, Quai
Jeanrenaud 3, Neuchâtel 2000, Switzerland
- Laboratory
of Bioanalytical Chemistry, University of
Neuchâtel, Neuchâtel 2000, Switzerland
| | - Philippe A. Guy
- Philip
Morris Product SA, Quai
Jeanrenaud 3, Neuchâtel 2000, Switzerland
| | - Sylvain Sutour
- Neuchâtel
Platform of Analytical Chemistry (NPAC), University of Neuchâtel, Neuchâtel 2000, Switzerland
| | - Manuel C. Peitsch
- Philip
Morris Product SA, Quai
Jeanrenaud 3, Neuchâtel 2000, Switzerland
| | - Nikolai V. Ivanov
- Philip
Morris Product SA, Quai
Jeanrenaud 3, Neuchâtel 2000, Switzerland
| | - Gaetan Glauser
- Neuchâtel
Platform of Analytical Chemistry (NPAC), University of Neuchâtel, Neuchâtel 2000, Switzerland
| | - Stephan von Reuss
- Laboratory
of Bioanalytical Chemistry, University of
Neuchâtel, Neuchâtel 2000, Switzerland
- Neuchâtel
Platform of Analytical Chemistry (NPAC), University of Neuchâtel, Neuchâtel 2000, Switzerland
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2
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Kiseleva OI, Kurbatov IY, Arzumanian VA, Ilgisonis EV, Zakharov SV, Poverennaya EV. The Expectation and Reality of the HepG2 Core Metabolic Profile. Metabolites 2023; 13:908. [PMID: 37623852 PMCID: PMC10456947 DOI: 10.3390/metabo13080908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/29/2023] [Accepted: 08/01/2023] [Indexed: 08/26/2023] Open
Abstract
To represent the composition of small molecules circulating in HepG2 cells and the formation of the "core" of characteristic metabolites that often attract researchers' attention, we conducted a meta-analysis of 56 datasets obtained through metabolomic profiling via mass spectrometry and NMR. We highlighted the 288 most commonly studied compounds of diverse chemical nature and analyzed metabolic processes involving these small molecules. Building a complete map of the metabolome of a cell, which encompasses the diversity of possible impacts on it, is a severe challenge for the scientific community, which is faced not only with natural limitations of experimental technologies, but also with the absence of transparent and widely accepted standards for processing and presenting the obtained metabolomic data. Formulating our research design, we aimed to reveal metabolites crucial to the Hepg2 cell line, regardless of all chemical and/or physical impact factors. Unfortunately, the existing paradigm of data policy leads to a streetlight effect. When analyzing and reporting only target metabolites of interest, the community ignores the changes in the metabolomic landscape that hide many molecular secrets.
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Affiliation(s)
- Olga I. Kiseleva
- Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia (E.V.I.); (E.V.P.)
| | - Ilya Y. Kurbatov
- Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia (E.V.I.); (E.V.P.)
| | - Viktoriia A. Arzumanian
- Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia (E.V.I.); (E.V.P.)
| | - Ekaterina V. Ilgisonis
- Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia (E.V.I.); (E.V.P.)
| | - Svyatoslav V. Zakharov
- Chemistry Department, Lomonosov Moscow State University, Leninskie gory Street, 1/3, 119991 Moscow, Russia;
| | - Ekaterina V. Poverennaya
- Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia (E.V.I.); (E.V.P.)
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3
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Mattoli L, Gianni M, Burico M. Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products. MASS SPECTROMETRY REVIEWS 2023; 42:1358-1396. [PMID: 35238411 DOI: 10.1002/mas.21773] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 11/16/2021] [Accepted: 02/11/2022] [Indexed: 06/07/2023]
Abstract
Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.
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Affiliation(s)
- Luisa Mattoli
- Department of Metabolomics & Analytical Sciences, Aboca SpA Società Agricola, Sansepolcro, AR, Italy
| | - Mattia Gianni
- Department of Metabolomics & Analytical Sciences, Aboca SpA Società Agricola, Sansepolcro, AR, Italy
| | - Michela Burico
- Department of Metabolomics & Analytical Sciences, Aboca SpA Società Agricola, Sansepolcro, AR, Italy
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4
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Consonni V, Gosetti F, Termopoli V, Todeschini R, Valsecchi C, Ballabio D. Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data. Molecules 2022; 27:5827. [PMID: 36144564 PMCID: PMC9502453 DOI: 10.3390/molecules27185827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/05/2022] [Indexed: 11/27/2022] Open
Abstract
Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.
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Affiliation(s)
| | | | | | | | | | - Davide Ballabio
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
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5
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Matsuda F, Komori S, Yamada Y, Hara D, Okahashi N. Data Processing of Product Ion Spectra: Quality Improvement by Averaging Multiple Similar Spectra of Small Molecules. Mass Spectrom (Tokyo) 2022; 11:A0106. [PMID: 36713802 PMCID: PMC9853114 DOI: 10.5702/massspectrometry.a0106] [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: 08/08/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022] Open
Abstract
In metabolomics studies using high-resolution mass spectrometry (MS), a set of product ion spectra is comprehensively acquired from observed ions using the data-dependent acquisition (DDA) mode of various tandem MS. However, especially for low-intensity signals, it is sometimes difficult to distinguish artifact signals from true fragment ions derived from a precursor ion. Inadequate precision in the measured m/z value is also one of the bottlenecks to narrowing down the candidate compositional formula. In this study, we report that averaging multiple product ion spectra can improve m/z precision as well as the reliability of fragment ions that are observed in such spectra. A graph-based method was applied to cluster a set of similar spectra from multiple DDA data files resulting in creating an averaged product-ion spectrum. The error levels for the m/z values declined following the central limit theorem, which allowed us to reduce the number of candidate compositional formulas. The improved reliability and precision of the averaged spectra will contribute to a more efficient annotation of product ion spectral data.
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Affiliation(s)
- Fumio Matsuda
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan,Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, Osaka, Japan,Correspondence to: Fumio Matsuda, Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1–5 Yamadaoka, Suita, Osaka 565–0871, Japan, e-mail:
| | - Shuka Komori
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Yuki Yamada
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Daiki Hara
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan
| | - Nobuyuki Okahashi
- Graduate School of Information Science and Technology, Osaka University, Osaka, Japan,Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, Osaka, Japan
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6
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Wasito H, Causon T, Hann S. Alternating in-source fragmentation with single-stage high-resolution mass spectrometry with high annotation confidence in non-targeted metabolomics. Talanta 2022; 236:122828. [PMID: 34635218 DOI: 10.1016/j.talanta.2021.122828] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/18/2021] [Accepted: 08/24/2021] [Indexed: 02/07/2023]
Abstract
Non-targeted metabolomics is increasingly applied in various applications for understanding biological processes and finding novel biomarkers in living organisms. However, high-confidence identity confirmation of metabolites in complex biological samples is still a significant bottleneck, especially when using single-stage mass analysers. In the current study, a complete workflow for alternating in-source fragmentation on a time-of-flight mass spectrometry (TOFMS) instrument for non-targeted metabolomics is presented. Hydrophilic interaction liquid chromatography (HILIC) was employed to assess polar metabolites in yeast following ESI parameter optimization using experimental design principles, which revealed the key influence of fragmentor voltage for this application. Datasets from alternating in-source fragmentation high resolution mass spectrometry (HRMS) were evaluated using open-source data processing tools combined with public reference mass spectral databases. The significant influence of the selected fragmentor voltages on the abundance of the primary analyte ion of interest and the extent of in-source fragmentation allowed an optimum selection of qualifier fragments for the different metabolites. The new acquisition and evaluation workflow was implemented for the non-targeted analysis of yeast extract samples whereby more than 130 metabolites were putatively annotated with more than 40% considered to be of high confidence. The presented workflow contains a fully elaborated acquisition and evaluation methodology using alternating in-source fragmentor voltages suitable for peak annotation and metabolite identity confirmation for non-targeted metabolomics applications performed on a single-stage HRMS platform.
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Affiliation(s)
- Hendri Wasito
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190, Vienna, Austria; Department of Pharmacy, Faculty of Health Sciences, Jenderal Soedirman University, Dr. Soeparno Street, 53122, Purwokerto, Indonesia
| | - Tim Causon
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190, Vienna, Austria
| | - Stephan Hann
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences, Vienna (BOKU), Muthgasse 18, 1190, Vienna, Austria.
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Sukmarini L. Recent Advances in Discovery of Lead Structures from Microbial Natural Products: Genomics- and Metabolomics-Guided Acceleration. Molecules 2021; 26:molecules26092542. [PMID: 33925414 PMCID: PMC8123854 DOI: 10.3390/molecules26092542] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/20/2021] [Accepted: 04/22/2021] [Indexed: 01/17/2023] Open
Abstract
Natural products (NPs) are evolutionarily optimized as drug-like molecules and remain the most consistently successful source of drugs and drug leads. They offer major opportunities for finding novel lead structures that are active against a broad spectrum of assay targets, particularly those from secondary metabolites of microbial origin. Due to traditional discovery approaches’ limitations relying on untargeted screening methods, there is a growing trend to employ unconventional secondary metabolomics techniques. Aided by the more in-depth understanding of different biosynthetic pathways and the technological advancement in analytical instrumentation, the development of new methodologies provides an alternative that can accelerate discoveries of new lead-structures of natural origin. This present mini-review briefly discusses selected examples regarding advancements in bioinformatics and genomics (focusing on genome mining and metagenomics approaches), as well as bioanalytics (mass-spectrometry) towards the microbial NPs-based drug discovery and development. The selected recent discoveries from 2015 to 2020 are featured herein.
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Affiliation(s)
- Linda Sukmarini
- Research Center for Biotechnology, Indonesian Institute of Sciences (LIPI), Cibinong, Bogor 16911, West Java, Indonesia
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8
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Augustijn D, de Groot HJM, Alia A. HR-MAS NMR Applications in Plant Metabolomics. Molecules 2021; 26:molecules26040931. [PMID: 33578691 PMCID: PMC7916392 DOI: 10.3390/molecules26040931] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/05/2021] [Accepted: 02/06/2021] [Indexed: 12/24/2022] Open
Abstract
Metabolomics is used to reduce the complexity of plants and to understand the underlying pathways of the plant phenotype. The metabolic profile of plants can be obtained by mass spectrometry or liquid-state NMR. The extraction of metabolites from the sample is necessary for both techniques to obtain the metabolic profile. This extraction step can be eliminated by making use of high-resolution magic angle spinning (HR-MAS) NMR. In this review, an HR-MAS NMR-based workflow is described in more detail, including used pulse sequences in metabolomics. The pre-processing steps of one-dimensional HR-MAS NMR spectra are presented, including spectral alignment, baseline correction, bucketing, normalisation and scaling procedures. We also highlight some of the models which can be used to perform multivariate analysis on the HR-MAS NMR spectra. Finally, applications of HR-MAS NMR in plant metabolomics are described and show that HR-MAS NMR is a powerful tool for plant metabolomics studies.
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Affiliation(s)
- Dieuwertje Augustijn
- Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands;
- Correspondence: (D.A.); (A.A.)
| | - Huub J. M. de Groot
- Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands;
| | - A. Alia
- Leiden Institute of Chemistry, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands;
- Institute of Medical Physics and Biophysics, University of Leipzig, Härtelstr. 16–17, D-04107 Leipzig, Germany
- Correspondence: (D.A.); (A.A.)
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9
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Khan T, Loftus TJ, Filiberto AC, Ozrazgat-Baslanti T, Ruppert MM, Bandhyopadyay S, Laiakis EC, Arnaoutakis DJ, Bihorac A. Metabolomic Profiling for Diagnosis and Prognostication in Surgery: A Scoping Review. Ann Surg 2021; 273:258-268. [PMID: 32482979 PMCID: PMC7704904 DOI: 10.1097/sla.0000000000003935] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE This review assimilates and critically evaluates available literature regarding the use of metabolomic profiling in surgical decision-making. BACKGROUND Metabolomic profiling is performed by nuclear magnetic resonance spectroscopy or mass spectrometry of biofluids and tissues to quantify biomarkers (ie, sugars, amino acids, and lipids), producing diagnostic and prognostic information that has been applied among patients with cardiovascular disease, inflammatory bowel disease, cancer, and solid organ transplants. METHODS PubMed was searched from 1995 to 2019 to identify studies investigating metabolomic profiling of surgical patients. Articles were included and assimilated into relevant categories per PRISMA-ScR guidelines. Results were summarized with descriptive analytical methods. RESULTS Forty-seven studies were included, most of which were retrospective studies with small sample sizes using various combinations of analytic techniques and types of biofluids and tissues. Results suggest that metabolomic profiling has the potential to effectively screen for surgical diseases, suggest diagnoses, and predict outcomes such as postoperative complications and disease recurrence. Major barriers to clinical adoption include a lack of high-level evidence from prospective studies, heterogeneity in study design regarding tissue and biofluid procurement and analytical methods, and the absence of large, multicenter metabolome databases to facilitate systematic investigation of the efficacy, reproducibility, and generalizability of metabolomic profiling diagnoses and prognoses. CONCLUSIONS Metabolomic profiling research would benefit from standardization of study design and analytic approaches. As technologies improve and knowledge garnered from research accumulates, metabolomic profiling has the potential to provide personalized diagnostic and prognostic information to support surgical decision-making from preoperative to postdischarge phases of care.
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Affiliation(s)
- Tabassum Khan
- Department of Surgery, University of Florida, Gainesville,
FL, USA
| | - Tyler J. Loftus
- Department of Surgery, University of Florida, Gainesville,
FL, USA
| | | | - Tezcan Ozrazgat-Baslanti
- Department of Medicine, University of Florida, Gainesville,
FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP),
University of Florida, Gainesville, FL
| | | | - Sabyasachi Bandhyopadyay
- Department of Medicine, University of Florida, Gainesville,
FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP),
University of Florida, Gainesville, FL
| | - Evagelia C. Laiakis
- Department of Oncology, Georgetown University, Washington
DC, USA
- Department of Biochemistry and Molecular & Cellular
Biology, Georgetown University, Washington DC, USA
| | | | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville,
FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP),
University of Florida, Gainesville, FL
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Mass Spectrometry-based Metabolomics in Translational Research. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1310:509-531. [PMID: 33834448 DOI: 10.1007/978-981-33-6064-8_19] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.
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Kanazawa S, Noda A, Ito A, Hashimoto K, Kunisawa A, Nakanishi T, Kajihara S, Mukai N, Iida J, Fukusaki E, Matsuda F. Fake metabolomics chromatogram generation for facilitating deep learning of peak-picking neural networks. J Biosci Bioeng 2020; 131:207-212. [PMID: 33051155 DOI: 10.1016/j.jbiosc.2020.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/14/2020] [Accepted: 09/21/2020] [Indexed: 10/23/2022]
Abstract
Finding peaks in chromatograms and determining their start and end points (peak picking) is a core task in chromatography based biotechnology. Construction of peak-picking neural networks by deep learning was, however, hampered from the preparation of exact peak-picked or "labeled" chromatograms since the exact start and end points were often unclear in overlapping peaks in real chromatograms. We present a design of a fake chromatogram generator, along with a method for deep learning of peak-picking neural networks. Fake chromatograms were generated by generation of fake peaks, random sampling of peak positions from feature distributions, and merging with real blank sample chromatograms. Information on the exact start and end points, as labeled on the fake chromatograms, were effective for training and evaluating peak-picking neural networks. The peak-picking neural networks constructed herein outperformed conventional peak-picking software and showed comparable performance with that of experienced operators for processing the widely targeted metabolome data. Results of this study indicate that generation of fake chromatograms would be crucial for developing peak-picking neural networks and a key technology for further improvement of peak picking neural networks.
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Affiliation(s)
- Shinji Kanazawa
- Shimadzu Corporation, 3-9-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan; Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, 2-1 Yamada-oka, Osaka 565-0871, Japan; Graduate School of Information Science and Technology, Osaka University, 2-1, Yamada-oka, Osaka 565-0871, Japan.
| | - Akira Noda
- Shimadzu Corporation, 3-9-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan.
| | - Arisa Ito
- Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, 2-1 Yamada-oka, Osaka 565-0871, Japan.
| | - Kyoko Hashimoto
- Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, 2-1 Yamada-oka, Osaka 565-0871, Japan.
| | - Akihiro Kunisawa
- Shimadzu Corporation, 3-9-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan.
| | - Tsuyoshi Nakanishi
- Shimadzu Corporation, 3-9-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan.
| | - Shigeki Kajihara
- Shimadzu Corporation, 3-9-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan.
| | - Norio Mukai
- Shimadzu Corporation, 3-9-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan.
| | - Junko Iida
- Shimadzu Corporation, 3-9-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan; Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, 2-1 Yamada-oka, Osaka 565-0871, Japan.
| | - Eiichiro Fukusaki
- Graduate School of Engineering, Osaka University, 1-5 Yamada-oka, Osaka 565-0871, Japan.
| | - Fumio Matsuda
- Graduate School of Information Science and Technology, Osaka University, 2-1, Yamada-oka, Osaka 565-0871, Japan.
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12
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Fan Z, Alley A, Ghaffari K, Ressom HW. MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation. Metabolomics 2020; 16:104. [PMID: 32997169 PMCID: PMC9547616 DOI: 10.1007/s11306-020-01726-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 09/19/2020] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds. OBJECTIVE The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries. METHODS We introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases using molecular formula or m/z value of the precursor ions of the analytes. The candidate whose fingerprint is most analogous to the predicted fingerprint is used for metabolite annotation. A comprehensive evaluation was performed by training MetFID using MS/MS spectra from the MoNA repository and NIST library and by testing with structure-disjoint MS/MS spectra from the NIST library, the CASMI 2016 dataset, and in-house MS/MS data from a cancer biomarker discovery study. RESULTS We observed that training separate models for distinct ranges of collision energies enhanced model performance compared to a single model that covers a wide range of collision energies. Using MetaboQuest to retrieve candidates, MetFID prioritized the correct putative ID in the first place rank for about 50% of the testing cases. Through the independent testing dataset, we demonstrated that MetFID has the potential to improve the accuracy of ranking putative metabolite IDs by more than 5% compared to other tools such as ChemDistiller, CSI:FingerID, and MetFrag. CONCLUSION MetFID offers a promising opportunity to enhance the accuracy of metabolite annotation by using ANN for molecular fingerprint prediction.
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Affiliation(s)
- Ziling Fan
- Department of Biochemistry and Molecular & Cellular Biology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Amber Alley
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Suite 173, Building D, 4000 Reservoir Road NW, Washington, DC, 20057, USA
| | - Kian Ghaffari
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Suite 173, Building D, 4000 Reservoir Road NW, Washington, DC, 20057, USA
| | - Habtom W Ressom
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Suite 173, Building D, 4000 Reservoir Road NW, Washington, DC, 20057, USA.
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Kuhring M, Eisenberger A, Schmidt V, Kränkel N, Leistner DM, Kirwan J, Beule D. Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies: MeTaQuaC. Anal Chem 2020; 92:10241-10245. [PMID: 32603093 DOI: 10.1021/acs.analchem.0c00136] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data are essential for confidence in analytical results and are particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory's routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire data set alongside standard analyses of quality control samples can be used to identify outliers and quantify systematic trends to improve downstream analysis. Finally, we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and other target data sets and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.
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Affiliation(s)
- Mathias Kuhring
- Berlin Institute of Health (BIH), Charitéplatz 1, 10117 Berlin, Germany.,Max Delbrück Center (MDC) for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany
| | - Alina Eisenberger
- Berlin Institute of Health (BIH), Charitéplatz 1, 10117 Berlin, Germany.,Max Delbrück Center (MDC) for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany
| | - Vanessa Schmidt
- Max Delbrück Center (MDC) for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany
| | - Nicolle Kränkel
- Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.,DZHK (German Centre for Cardiovascular Research) Partner Site Berlin, Potsdamer Strasse 58, 10785 Berlin, Germany
| | - David M Leistner
- Berlin Institute of Health (BIH), Charitéplatz 1, 10117 Berlin, Germany.,Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.,DZHK (German Centre for Cardiovascular Research) Partner Site Berlin, Potsdamer Strasse 58, 10785 Berlin, Germany
| | - Jennifer Kirwan
- Berlin Institute of Health (BIH), Charitéplatz 1, 10117 Berlin, Germany.,Max Delbrück Center (MDC) for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany
| | - Dieter Beule
- Berlin Institute of Health (BIH), Charitéplatz 1, 10117 Berlin, Germany.,Max Delbrück Center (MDC) for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany.,Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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14
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Folberth J, Begemann K, Jöhren O, Schwaninger M, Othman A. MS 2 and LC libraries for untargeted metabolomics: Enhancing method development and identification confidence. J Chromatogr B Analyt Technol Biomed Life Sci 2020; 1145:122105. [PMID: 32305706 DOI: 10.1016/j.jchromb.2020.122105] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 03/25/2020] [Accepted: 04/03/2020] [Indexed: 12/31/2022]
Abstract
As part of the "omics" technologies in the life sciences, metabolomics is becoming increasingly important. In untargeted metabolomics, unambiguous metabolite identification and the inevitable coverage bias that comes with the selection of analytical conditions present major challenges. Reliable compound annotation is essential for translating metabolomics data into meaningful biological information. Here, we developed a fast and transferable method for generating in-house MS2 libraries to improve metabolite identification. Using the new method we established an in-house MS2 library that includes over 4,000 fragmentation spectra of 506 standard compounds for 6 different normalized collision energies (NCEs). Additionally, we generated a comprehensive liquid chromatography (LC) library by testing 57 different LC-MS conditions for 294 compounds. We used the library information to develop an untargeted metabolomics screen with maximum coverage of the metabolome that was successfully tested in a study of 360 human serum samples. The current work demonstrates a workflow for LC-MS/MS-based metabolomics, with enhanced metabolite identification confidence and the possibility to select suitable analysis conditions according to the specific research interest.
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Affiliation(s)
- Julica Folberth
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany; German Research Centre for Cardiovascular Research (DZHK), partner site Hamburg/Lübeck, Kiel, Germany
| | - Kimberly Begemann
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany
| | - Olaf Jöhren
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany; Bioanalytic Core Facility, Center for Brain Behavior and Metabolism, University of Lübeck, Lübeck, Germany
| | - Markus Schwaninger
- Institute for Experimental and Clinical Pharmacology and Toxicology, University of Lübeck, Lübeck, Germany; German Research Centre for Cardiovascular Research (DZHK), partner site Hamburg/Lübeck, Kiel, Germany; Department of Neurology, University of Heidelberg, Heidelberg, Germany.
| | - Alaa Othman
- Bioanalytic Core Facility, Center for Brain Behavior and Metabolism, University of Lübeck, Lübeck, Germany.
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15
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Current trends in isotope‐coded derivatization liquid chromatographic‐mass spectrometric analyses with special emphasis on their biomedical application. Biomed Chromatogr 2020; 34:e4756. [DOI: 10.1002/bmc.4756] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 11/13/2019] [Accepted: 11/18/2019] [Indexed: 12/17/2022]
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16
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Yu J, Zhao J, Zhang M, Guo J, Liu X, Liu L. Metabolomics studies in gastrointestinal cancer: a systematic review. Expert Rev Gastroenterol Hepatol 2020; 14:9-25. [PMID: 31786962 DOI: 10.1080/17474124.2020.1700112] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Introduction: This systemic review provides an overview of metabolic perturbations and possible mechanisms in gastrointestinal cancer. The authors discuss emerging challenges of technical and clinical applications.Areas covered: In this systemic review, the authors summarized the currently available results of metabolomic biomarkers linked to GI cancer, and discussed the altered metabolism pathways including carbohydrate metabolism, amino acid metabolism, lipids, and nucleotide metabolism and other metabolisms. Furthermore, future efforts need to adhere to normalize analysis procedures, validate with the larger cohort and utilize multiple-omics technologies. The search was conducted in PubMed with the following search terms (biomarker, gastrointestinal cancer, colorectal cancer, and esophageal cancer) from 2013 to 2019.Expert opinion: This systemic review summarized the currently available results of metabolomic biomarkers linked to gastrointestinal cancer, and discussed the altered metabolism pathways. The authors believe that metabolomics will benefit deeper understandings of the pathogenic mechanism, discovery of biomarkers and aid the search for drug targets as we move toward the era of personalized medicine. Personalized medication for tumors can improve the curative effect, avoid side effects and medical resource waste. As a promisingtool, metabolomics that targets the entire cancer-specific metabolite network should be applied more widely in cancer research.
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Affiliation(s)
- Jiaying Yu
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Jinhui Zhao
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Mingjia Zhang
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Jing Guo
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Xiaowei Liu
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
| | - Liyan Liu
- Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, P. R. China
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17
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Andraos S, Wake M, Saffery R, Burgner D, Kussmann M, O'Sullivan J. Perspective: Advancing Understanding of Population Nutrient-Health Relations via Metabolomics and Precision Phenotypes. Adv Nutr 2019; 10:944-952. [PMID: 31098626 PMCID: PMC6855971 DOI: 10.1093/advances/nmz045] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 03/26/2019] [Accepted: 04/01/2019] [Indexed: 02/07/2023] Open
Abstract
Diet and lifestyle are vital to population health, but their true contribution is difficult to quantify using traditional methods. Nutrient-health relations are typically based on epidemiological associations that are assessed at the population level, traditionally using self-reported dietary and lifestyle data. Unfortunately, such measures are inherently inaccurate. New technologies such as metabolomics can measure nutritional and micronutrient profiles in body fluids, providing objective evaluation of nutritional status. A critical step toward accurate health prediction models would be the building of integrated repositories of nutritional measures combining subjective methods of reporting with objective metabolomics profiles and precise phenotypic data. Here we outline a roadmap to achieve this goal and discuss both the advantages and risks of this approach. We also highlight the uncertain associations between the complexity of high-dimensional data generated in 'omics research (along with the public confusion this may engender) and the rapid adoption of 'omics approaches by nutrition and health companies to develop nutritional products and services.
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Affiliation(s)
| | - Melissa Wake
- The Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Richard Saffery
- The Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - David Burgner
- The Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Martin Kussmann
- Liggins Institute, Auckland, New Zealand,New Zealand National Science Challenge, High-Value Nutrition, The University of Auckland, Auckland, New Zealand,Frontiers Media SA, Lausanne, Switzerland
| | - Justin O'Sullivan
- Liggins Institute, Auckland, New Zealand,New Zealand National Science Challenge, High-Value Nutrition, The University of Auckland, Auckland, New Zealand,Address correspondence to JO (e-mail: )
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18
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Manzi M, Riquelme G, Zabalegui N, Monge ME. Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics. J Pharm Biomed Anal 2019; 178:112905. [PMID: 31707200 DOI: 10.1016/j.jpba.2019.112905] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 09/27/2019] [Accepted: 10/01/2019] [Indexed: 12/24/2022]
Abstract
The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.
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Affiliation(s)
- Malena Manzi
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina; Departamento de Química Biológica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Junín 956, C1113AAD, Ciudad de Buenos Aires, Argentina
| | - Gabriel Riquelme
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina; Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina
| | - Nicolás Zabalegui
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina; Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina.
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19
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Acid Mine Drainage as Habitats for Distinct Microbiomes: Current Knowledge in the Era of Molecular and Omic Technologies. Curr Microbiol 2019; 77:657-674. [DOI: 10.1007/s00284-019-01771-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 09/09/2019] [Indexed: 11/27/2022]
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20
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Elpa DP, Prabhu GRD, Wu SP, Tay KS, Urban PL. Automation of mass spectrometric detection of analytes and related workflows: A review. Talanta 2019; 208:120304. [PMID: 31816721 DOI: 10.1016/j.talanta.2019.120304] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 08/26/2019] [Accepted: 08/28/2019] [Indexed: 12/13/2022]
Abstract
The developments in mass spectrometry (MS) in the past few decades reveal the power and versatility of this technology. MS methods are utilized in routine analyses as well as research activities involving a broad range of analytes (elements and molecules) and countless matrices. However, manual MS analysis is gradually becoming a thing of the past. In this article, the available MS automation strategies are critically evaluated. Automation of analytical workflows culminating with MS detection encompasses involvement of automated operations in any of the steps related to sample handling/treatment before MS detection, sample introduction, MS data acquisition, and MS data processing. Automated MS workflows help to overcome the intrinsic limitations of MS methodology regarding reproducibility, throughput, and the expertise required to operate MS instruments. Such workflows often comprise automated off-line and on-line steps such as sampling, extraction, derivatization, and separation. The most common instrumental tools include autosamplers, multi-axis robots, flow injection systems, and lab-on-a-chip. Prototyping customized automated MS systems is a way to introduce non-standard automated features to MS workflows. The review highlights the enabling role of automated MS procedures in various sectors of academic research and industry. Examples include applications of automated MS workflows in bioscience, environmental studies, and exploration of the outer space.
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Affiliation(s)
- Decibel P Elpa
- Department of Applied Chemistry, National Chiao Tung University, 1001 University Rd., Hsinchu, 300, Taiwan; Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Rd., Hsinchu, 30013, Taiwan
| | - Gurpur Rakesh D Prabhu
- Department of Applied Chemistry, National Chiao Tung University, 1001 University Rd., Hsinchu, 300, Taiwan; Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Rd., Hsinchu, 30013, Taiwan
| | - Shu-Pao Wu
- Department of Applied Chemistry, National Chiao Tung University, 1001 University Rd., Hsinchu, 300, Taiwan.
| | - Kheng Soo Tay
- Department of Chemistry, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Pawel L Urban
- Department of Chemistry, National Tsing Hua University, 101, Section 2, Kuang-Fu Rd., Hsinchu, 30013, Taiwan; Frontier Research Center on Fundamental and Applied Sciences of Matters, National Tsing Hua University, 101, Section 2, Kuang-Fu Rd., Hsinchu, 30013, Taiwan.
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21
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Nishidono Y, Chiyomatsu T, Sanuki K, Tezuka Y, Tanaka K. Analysis of Seasonal Variations of the Volatile Constituents in Artemisia princeps (Japanese Mugwort) Leaves by Metabolomic Approach. Nat Prod Commun 2019. [DOI: 10.1177/1934578x19872600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The leaves of Artemisia princeps (Japanese mugwort, “Yomogi”) are traditionally used as a food ingredient to provide a fresh aroma and a deep green color. In this study, the seasonal variations of the volatile constituents in Japanese mugwort leaves collected from several parts of the plants were investigated using gas chromatography and multivariate analysis in order to determine the best time to harvest them and the best parts of the plants from which the leaves can be gathered and utilized as an ingredient in food. As a result, it was clarified that the balance between the amounts of monoterpenes and sesquiterpenes is an important factor that determines the quality of Japanese mugwort. In addition, the amount of β-caryophyllene was found to be the important factor that determines the best time and from which parts of the plant to harvest high-quality Japanese mugwort.
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Affiliation(s)
- Yuto Nishidono
- College of Pharmaceutical Sciences, Ritsumeikan University, Kusatsu, Japan
| | | | | | - Yasuhiro Tezuka
- Faculty of Pharmaceutical Sciences, Hokuriku University, Kanazawa, Japan
| | - Ken Tanaka
- College of Pharmaceutical Sciences, Ritsumeikan University, Kusatsu, Japan
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22
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Kennedy E, Arcadia CE, Geiser J, Weber PM, Rose C, Rubenstein BM, Rosenstein JK. Encoding information in synthetic metabolomes. PLoS One 2019; 14:e0217364. [PMID: 31269053 PMCID: PMC6608926 DOI: 10.1371/journal.pone.0217364] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Accepted: 05/10/2019] [Indexed: 12/17/2022] Open
Abstract
Biomolecular information systems offer exciting potential advantages and opportunities to complement conventional semiconductor technologies. Much attention has been paid to information-encoding polymers, but small molecules also play important roles in biochemical information systems. Downstream from DNA, the metabolome is an information-rich molecular system with diverse chemical dimensions which could be harnessed for information storage and processing. As a proof of principle of small-molecule postgenomic data storage, here we demonstrate a workflow for representing abstract data in synthetic mixtures of metabolites. Our approach leverages robotic liquid handling for writing digital information into chemical mixtures, and mass spectrometry for extracting the data. We present several kilobyte-scale image datasets stored in synthetic metabolomes, which can be decoded with accuracy exceeding 99% using multi-mass logistic regression. Cumulatively, >100,000 bits of digital image data was written into metabolomes. These early demonstrations provide insight into some of the benefits and limitations of small-molecule chemical information systems.
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Affiliation(s)
- Eamonn Kennedy
- School of Engineering, Brown University, Providence, RI, United States of America
| | | | - Joseph Geiser
- Department of Chemistry, Brown University, Providence, RI, United States of America
| | - Peter M. Weber
- Department of Chemistry, Brown University, Providence, RI, United States of America
| | - Christopher Rose
- School of Engineering, Brown University, Providence, RI, United States of America
| | - Brenda M. Rubenstein
- Department of Chemistry, Brown University, Providence, RI, United States of America
| | - Jacob K. Rosenstein
- School of Engineering, Brown University, Providence, RI, United States of America
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23
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Caesar LK, Kellogg JJ, Kvalheim OM, Cech NB. Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures. JOURNAL OF NATURAL PRODUCTS 2019; 82:469-484. [PMID: 30844279 PMCID: PMC6837904 DOI: 10.1021/acs.jnatprod.9b00176] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Compounds derived from natural sources represent the majority of small-molecule drugs utilized today. Plants, owing to their complex biosynthetic pathways, are poised to synthesize diverse secondary metabolites that selectively target biological macromolecules. Despite the vast chemical landscape of botanicals, drug discovery programs from these sources have diminished due to the costly and time-consuming nature of standard practices and high rates of compound rediscovery. Untargeted metabolomics approaches that integrate biological and chemical data sets potentially enable the prediction of active constituents early in the fractionation process. However, data acquisition and data processing parameters may have major impacts on the success of models produced. Using an inactive botanical mixture spiked with known antimicrobial compounds, untargeted mass spectrometry-based metabolomics data were combined with bioactivity data to produce selectivity ratio models subjected to a variety of data acquisition and data processing parameters. Selectivity ratio models were used to identify active constituents that were intentionally added to the mixture, along with an additional antimicrobial compound, randainal (5), which was masked by the presence of antagonists in the mixture. These studies found that data-processing approaches, particularly data transformation and model simplification tools using a variance cutoff, had significant impacts on the models produced, either masking or enhancing the ability to detect active constituents in samples. The current study highlights the importance of the data processing step for obtaining reliable information from metabolomics models and demonstrates the strengths and limitations of selectivity ratio analysis to comprehensively assess complex botanical mixtures.
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Affiliation(s)
- Lindsay K. Caesar
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, NC 27402, United States
| | - Joshua J. Kellogg
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, NC 27402, United States
| | | | - Nadja B. Cech
- Department of Chemistry & Biochemistry, University of North Carolina Greensboro, Greensboro, NC 27402, United States
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24
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Chu HW, Unnikrishnan B, Anand A, Mao JY, Huang CC. Nanoparticle-based laser desorption/ionization mass spectrometric analysis of drugs and metabolites. J Food Drug Anal 2018; 26:1215-1228. [PMID: 30249320 PMCID: PMC9298562 DOI: 10.1016/j.jfda.2018.07.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 06/22/2018] [Accepted: 07/19/2018] [Indexed: 12/26/2022] Open
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25
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Hsiao JJ, Potter OG, Chu TW, Yin H. Improved LC/MS Methods for the Analysis of Metal-Sensitive Analytes Using Medronic Acid as a Mobile Phase Additive. Anal Chem 2018; 90:9457-9464. [PMID: 29976062 DOI: 10.1021/acs.analchem.8b02100] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Phosphorylated compounds and organic acids with multiple carboxylate groups are commonly observed to have poor peak shapes and signal in LC/MS experiments. The poor peak shape is caused by the presence of trace metals, particularly iron, contributed from a variety of sources within the chromatographic system. To ameliorate this problem, different solvent additives were investigated to reduce the amount of metal in the flow path to achieve better analytical performance for these metal-sensitive compounds. Here, we introduce the use of a solvent additive that can significantly improve the peak shapes and signal of metal-sensitive metabolites for LC/MS analysis. Moreover, the additive is shown to be amenable for other metal-sensitive applications, such as the analysis of phosphopeptides and polar phosphorylated pesticides, where the instruments could be used in either positive or negative analysis mode.
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Affiliation(s)
- Jordy J Hsiao
- Agilent Technologies, Santa Clara , California 95051 , United States
| | - Oscar G Potter
- Agilent Technologies, Santa Clara , California 95051 , United States
| | - Te-Wei Chu
- Agilent Technologies, Santa Clara , California 95051 , United States
| | - Hongfeng Yin
- Agilent Technologies, Santa Clara , California 95051 , United States
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26
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The impact of genome variation and diet on the metabolic phenotype and microbiome composition of Drosophila melanogaster. Sci Rep 2018; 8:6215. [PMID: 29670218 PMCID: PMC5906449 DOI: 10.1038/s41598-018-24542-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 04/05/2018] [Indexed: 12/19/2022] Open
Abstract
The metabolic phenotype of an organism depends on a complex regulatory network, which integrates the plethora of intrinsic and external information and prioritizes the flow of nutrients accordingly. Given the rise of metabolic disorders including obesity, a detailed understanding of this regulatory network is in urgent need. Yet, our level of understanding is far from completeness and complicated by the discovery of additional layers in metabolic regulation, such as the impact of the microbial community present in the gut on the hosts’ energy storage levels. Here, we investigate the interplay between genome variation, diet and the gut microbiome in the shaping of a metabolic phenotype. For this purpose, we reared a set of fully sequenced wild type Drosophila melanogaster flies under basal and nutritionally challenged conditions and performed metabolic and microbiome profiling experiments. Our results introduce the fly as a model system to investigate the impact of genome variation on the metabolic response to diet alterations and reveal candidate single nucleotide polymorphisms associated with different metabolic traits, as well as metabolite-metabolite and metabolite-microbe correlations. Intriguingly, the dietary changes affected the microbiome composition less than anticipated. These results challenge the current view of a rapidly changing microbiome in response to environmental fluctuations.
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27
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Hierarchical cluster analysis of technical replicates to identify interferents in untargeted mass spectrometry metabolomics. Anal Chim Acta 2018; 1021:69-77. [PMID: 29681286 DOI: 10.1016/j.aca.2018.03.013] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 03/03/2018] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in all data sets. This work highlights the importance of technical replication in mass spectrometry-based studies, and presents a new application of HCA as a tool for evaluating the effectiveness of data filtering prior to statistical analysis.
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28
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Matsuda F, Toya Y, Shimizu H. Learning from quantitative data to understand central carbon metabolism. Biotechnol Adv 2017; 35:971-980. [DOI: 10.1016/j.biotechadv.2017.09.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 09/01/2017] [Accepted: 09/14/2017] [Indexed: 12/23/2022]
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Llano SM, Muñoz-Jiménez AM, Jiménez-Cartagena C, Londoño-Londoño J, Medina S. Untargeted metabolomics reveals specific withanolides and fatty acyl glycoside as tentative metabolites to differentiate organic and conventional Physalis peruviana fruits. Food Chem 2017; 244:120-127. [PMID: 29120759 DOI: 10.1016/j.foodchem.2017.10.026] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 09/23/2017] [Accepted: 10/06/2017] [Indexed: 01/15/2023]
Abstract
The agronomic production systems may affect the levels of food metabolites. Metabolomics approaches have been applied as useful tool for the characterization of fruit metabolome. In this study, metabolomics techniques were used to assess the differences in phytochemical composition between goldenberry samples produced by organic and conventional systems. To verify that the organic samples were free of pesticides, individual pesticides were analyzed. Principal component analysis showed a clear separation of goldenberry samples from two different farming systems. Via targeted metabolomics assays, whereby carotenoids and ascorbic acid were analyzed, not statistical differences between both crops were found. Conversely, untargeted metabolomics allowed us to identify two withanolides and one fatty acyl glycoside as tentative metabolites to differentiate goldenberry fruits, recording organic fruits higher amounts of these compounds than conventional samples. Hence, untargeted metabolomics technology could be suitable to research differences on phytochemicals under different agricultural management practices and to authenticate organic products.
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Affiliation(s)
- Sandra M Llano
- Faculty of Engineering, Food Engineering Program, Corporación Universitaria Lasallista, Caldas-Antioquia, Colombia
| | - Ana M Muñoz-Jiménez
- Faculty of Engineering, Food Engineering Program, Corporación Universitaria Lasallista, Caldas-Antioquia, Colombia
| | - Claudio Jiménez-Cartagena
- Faculty of Engineering, Food Engineering Program, Corporación Universitaria Lasallista, Caldas-Antioquia, Colombia
| | - Julián Londoño-Londoño
- Faculty of Engineering, Food Engineering Program, Corporación Universitaria Lasallista, Caldas-Antioquia, Colombia
| | - Sonia Medina
- Faculty of Engineering, Food Engineering Program, Corporación Universitaria Lasallista, Caldas-Antioquia, Colombia.
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30
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Böcker S. Searching molecular structure databases using tandem MS data: are we there yet? Curr Opin Chem Biol 2017; 36:1-6. [DOI: 10.1016/j.cbpa.2016.12.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 12/06/2016] [Accepted: 12/07/2016] [Indexed: 10/20/2022]
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