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Perez de Souza L, Fernie AR. Computational methods for processing and interpreting mass spectrometry-based metabolomics. Essays Biochem 2024; 68:5-13. [PMID: 37999335 PMCID: PMC11065554 DOI: 10.1042/ebc20230019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/10/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Abstract
Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.
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Affiliation(s)
- Leonardo Perez de Souza
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
- Center for Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
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2
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Cajka T, Hricko J, Rakusanova S, Brejchova K, Novakova M, Rudl Kulhava L, Hola V, Paucova M, Fiehn O, Kuda O. Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics. Int J Mol Sci 2024; 25:2899. [PMID: 38474147 DOI: 10.3390/ijms25052899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/17/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.
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Affiliation(s)
- Tomas Cajka
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Jiri Hricko
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Stanislava Rakusanova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Kristyna Brejchova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Michaela Novakova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Lucie Rudl Kulhava
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Veronika Hola
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Michaela Paucova
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Ondrej Kuda
- Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 14200 Prague, Czech Republic
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3
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Thukral M, Allen AE, Petras D. Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics. ISME J 2023; 17:2147-2159. [PMID: 37857709 PMCID: PMC10689791 DOI: 10.1038/s41396-023-01532-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 09/27/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023]
Abstract
Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.
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Affiliation(s)
- Monica Thukral
- University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, USA
- J. Craig Venter Institute, Microbial and Environmental Genomics Group, La Jolla, CA, USA
| | - Andrew E Allen
- University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, USA
- J. Craig Venter Institute, Microbial and Environmental Genomics Group, La Jolla, CA, USA
| | - Daniel Petras
- University of Tuebingen, CMFI Cluster of Excellence, Tuebingen, Germany.
- University of California Riverside, Department of Biochemistry, Riverside, CA, USA.
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Hoffmann MA, Kretschmer F, Ludwig M, Böcker S. MAD HATTER Correctly Annotates 98% of Small Molecule Tandem Mass Spectra Searching in PubChem. Metabolites 2023; 13:314. [PMID: 36984753 PMCID: PMC10053663 DOI: 10.3390/metabo13030314] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 02/23/2023] Open
Abstract
Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics usually relies on mass spectrometry, a technology capable of detecting thousands of compounds in a biological sample. Metabolite annotation is executed using tandem mass spectrometry. Spectral library search is far from comprehensive, and numerous compounds remain unannotated. So-called in silico methods allow us to overcome the restrictions of spectral libraries, by searching in much larger molecular structure databases. Yet, after more than a decade of method development, in silico methods still do not reach the correct annotation rates that users would wish for. Here, we present a novel computational method called Mad Hatter for this task. Mad Hatter combines CSI:FingerID results with information from the searched structure database via a metascore. Compound information includes the melting point, and the number of words in the compound description starting with the letter ‘u’. We then show that Mad Hatter reaches a stunning 97.6% correct annotations when searching PubChem, one of the largest and most comprehensive molecular structure databases. Unfortunately, Mad Hatter is not a real method. Rather, we developed Mad Hatter solely for the purpose of demonstrating common issues in computational method development and evaluation. We explain what evaluation glitches were necessary for Mad Hatter to reach this annotation level, what is wrong with similar metascores in general, and why metascores may screw up not only method evaluations but also the analysis of biological experiments. This paper may serve as an example of problems in the development and evaluation of machine learning models for metabolite annotation.
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Chandran M, S S, Abhirami, Chandran A, Jaleel A, Plakkal Ayyappan J. Defining atherosclerotic plaque biology by mass spectrometry-based omics approaches. Mol Omics 2023; 19:6-26. [PMID: 36426765 DOI: 10.1039/d2mo00260d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Atherosclerosis is the principal cause of vascular diseases and one of the leading causes of worldwide death. Even though several insights into its natural course, risk factors and interventions have been identified, it is still an ongoing global pandemic. Since the structure and biochemical composition of the plaques show high heterogeneity, a comprehensive understanding of the intraplaque composition, its microenvironment, and the mechanisms of the progression and instability across different vascular beds at their progression stages is crucial for better risk stratification and treatment modalities. Even though several cell-based studies, animal studies, and extensive multicentric population studies have been conducted concerning cardiovascular diseases for assessing the risk factors and plaque biology, the studies on human clinical samples are very limited. New novel approaches utilize samples from percutaneous coronary interventions, which could possibly gain more access to clinical samples at different stages of the diseases without complex invasive resections. As an emerging technological platform in disease discovery research, mass spectrometry-based omics technologies offer capabilities for a comprehensive understanding of the mechanisms linked to several vascular diseases. Here, we discuss the cellular and molecular processes of atherosclerosis, different mass spectrometry-based omics approaches, and the studies mostly done on clinical samples of atheroma plaque using mass spectrometry-based proteomics, metabolomics and lipidomics approaches.
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Affiliation(s)
- Mahesh Chandran
- Translational Nanomedicine and Lifestyle Disease Research Laboratory, Department of Biochemistry, University of Kerala, Thiruvananthapuram 695034, Kerala, India. .,Department of Biotechnology, University of Kerala, Thiruvananthapuram 695034, Kerala, India.,Mass Spectrometry and Proteomics Core Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, 695012, India
| | - Sudhina S
- Translational Nanomedicine and Lifestyle Disease Research Laboratory, Department of Biochemistry, University of Kerala, Thiruvananthapuram 695034, Kerala, India.
| | - Abhirami
- Translational Nanomedicine and Lifestyle Disease Research Laboratory, Department of Biochemistry, University of Kerala, Thiruvananthapuram 695034, Kerala, India.
| | - Akash Chandran
- Department of Nanoscience and Nanotechnology, University of Kerala, Kariavattom, Thiruvananthapuram-695581, Kerala, India
| | - Abdul Jaleel
- Mass Spectrometry and Proteomics Core Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, 695012, India
| | - Janeesh Plakkal Ayyappan
- Translational Nanomedicine and Lifestyle Disease Research Laboratory, Department of Biochemistry, University of Kerala, Thiruvananthapuram 695034, Kerala, India. .,Department of Biotechnology, University of Kerala, Thiruvananthapuram 695034, Kerala, India.,Department of Nanoscience and Nanotechnology, University of Kerala, Kariavattom, Thiruvananthapuram-695581, Kerala, India.,Centre for Advanced Cancer Research, Department of Biochemistry, University of Kerala, Thiruvananthapuram 695034, Kerala, India
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Jiang Z, Deng C, Zhou L, Wang Z, Wang F, Wu X, Ma X, Nan Z. High‐performance liquid chromatography coupled with quadrupole time‐of‐flight tandem mass spectrometry for profiling diterpenoid alkaloids in Aconitum species. Separation Science Plus 2022. [DOI: 10.1002/sscp.202200100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Zhi‐Bo Jiang
- Department of Pharmaceutical Engineering School of Chemistry and Chemical Engineering Key Laboratory of Chemical Engineering and Technology of State Ethnic Affairs Commission North Minzu University Yinchuan P. R. China
- Ningxia Low‐grade Resource High‐value Utilization and Environmental Chemical Integration Technology Innovation Team Project Yinchuan P. R. China
| | - Chao‐Fan Deng
- Department of Pharmaceutical Engineering School of Chemistry and Chemical Engineering Key Laboratory of Chemical Engineering and Technology of State Ethnic Affairs Commission North Minzu University Yinchuan P. R. China
| | - Le‐Ru Zhou
- Department of Pharmaceutical Engineering School of Chemistry and Chemical Engineering Key Laboratory of Chemical Engineering and Technology of State Ethnic Affairs Commission North Minzu University Yinchuan P. R. China
| | - Zhen‐Zhen Wang
- Department of Pharmaceutical Engineering School of Chemistry and Chemical Engineering Key Laboratory of Chemical Engineering and Technology of State Ethnic Affairs Commission North Minzu University Yinchuan P. R. China
| | - Fang Wang
- Shandong Academy of Pharmaceutical Sciences Jinan P. R. China
| | - Xiu‐Li Wu
- College of Pharmacy Ningxia Medical University Yinchuan P. R. China
| | - Xiao‐Li Ma
- Department of Pharmaceutical Engineering School of Chemistry and Chemical Engineering Key Laboratory of Chemical Engineering and Technology of State Ethnic Affairs Commission North Minzu University Yinchuan P. R. China
| | - Ze‐Dong Nan
- Department of Pharmaceutical Engineering School of Chemistry and Chemical Engineering Key Laboratory of Chemical Engineering and Technology of State Ethnic Affairs Commission North Minzu University Yinchuan P. R. China
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da Silva KM, van de Lavoir M, Robeyns R, Iturrospe E, Verheggen L, Covaci A, van Nuijs ALN. Guidelines and considerations for building multidimensional libraries for untargeted MS-based metabolomics. Metabolomics 2022; 19:4. [PMID: 36576608 DOI: 10.1007/s11306-022-01965-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/05/2022] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Feature annotation is crucial in untargeted metabolomics but remains a major challenge. The large pool of metabolites collected under various instrumental conditions is underrepresented in publicly available databases. Retention time (RT) and collision cross section (CCS) measurements from liquid chromatography ion mobility high-resolution mass spectrometers can be employed in addition to MS/MS spectra to improve the confidence of metabolite annotation. Recent advancements in machine learning focus on improving the accuracy of predictions for CCS and RT values. Therefore, high-quality experimental data are crucial to be used either as training datasets or as a reference for high-confidence matching. METHODS This manuscript provides an easy-to-use workflow for the creation of an in-house metabolite library, offers an overview of alternative solutions, and discusses the challenges and advantages of using open-source software. A total of 100 metabolite standards from various classes were analyzed and subjected to the described workflow for library generation. RESULTS AND DISCUSSION The outcome was an open-access available NIST format metabolite library (.msp) with multidimensional information. The library was used to evaluate CCS prediction tools, MS/MS spectra heterogeneities (e.g., multiple adducts, in-source fragmentation, radical fragment ions using collision-induced dissociation), and the reporting of RT.
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Affiliation(s)
- Katyeny Manuela da Silva
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Maria van de Lavoir
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Rani Robeyns
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Elias Iturrospe
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
- Department of In Vitro Toxicology and Dermato-Cosmetology, Faculty of Medicine and Pharmacy, Campus Jette, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium
| | - Lisa Verheggen
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Adrian Covaci
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Alexander L N van Nuijs
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium.
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Bittremieux W, Wang M, Dorrestein PC. The critical role that spectral libraries play in capturing the metabolomics community knowledge. Metabolomics 2022; 18:94. [PMID: 36409434 DOI: 10.1007/s11306-022-01947-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/19/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments. AIM OF REVIEW We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries. KEY SCIENTIFIC CONCEPTS OF REVIEW This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.
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Affiliation(s)
- Wout Bittremieux
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Mingxun Wang
- Department of Computer Science, University of California Riverside, Riverside, CA, 92507, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, 92093, USA.
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA.
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Bremer PL, Vaniya A, Kind T, Wang S, Fiehn O. How Well Can We Predict Mass Spectra from Structures? Benchmarking Competitive Fragmentation Modeling for Metabolite Identification on Untrained Tandem Mass Spectra. J Chem Inf Model 2022; 62:4049-4056. [PMID: 36043939 DOI: 10.1021/acs.jcim.2c00936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) is a machine learning tool to predict in silico tandem mass spectra (MS/MS) for known or suspected metabolites for which chemical reference standards are not available. As a machine learning tool, it relies on both an underlying statistical model and an explicit training set that encompasses experimental mass spectra for specific compounds. Such mass spectra depend on specific parameters such as collision energies, instrument types, and adducts which are accumulated in libraries. Yet, ultimately prediction tools that are meant to cover wide expanses of entities must be validated on cases that were not included in the initial training and testing sets. Hence, we here benchmarked the performance of CFM-ID 4.0 to correctly predict MS/MS spectra for spectra that were not included in the CFM-ID training set and for different mass spectrometry conditions. We used 609,456 experimental tandem spectra from the NIST20 mass spectral library that were newly added to the previous NIST17 library version. We found that CFM-ID's highest energy prediction output would maximize the capacity for library generation. Matching the experimental collision energy with CFM-ID's prediction energy produced the best results, even for HCD-Orbitrap instruments. For benzenoids, better MS/MS predictions were achieved than for heterocyclic compounds. However, when exploring CFM-ID's performance on 8,305 compounds at 40 eV HCD-Orbitrap collision energy, >90% of the 20/80 split test compounds showed <700 MS/MS similarity score. Instead of a stand-alone tool, CFM-ID 4.0 might be useful to boost candidate structures in the greater context of identification workflows.
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Affiliation(s)
- Parker Ladd Bremer
- Department of Chemistry, University of California Davis, Davis, California 95616, United States
| | - Arpana Vaniya
- West Coast Metabolomics Center for Compound Identification, UC Davis Genome Center, University of California Davis, Davis, California 95616, United States
| | - Tobias Kind
- West Coast Metabolomics Center for Compound Identification, UC Davis Genome Center, University of California Davis, Davis, California 95616, United States
| | - Shunyang Wang
- Department of Chemistry, University of California Davis, Davis, California 95616, United States
| | - Oliver Fiehn
- West Coast Metabolomics Center for Compound Identification, UC Davis Genome Center, University of California Davis, Davis, California 95616, United States
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Milman BL, Zhurkovich IK. Present-Day Practice of Non-Target Chemical Analysis. J Anal Chem 2022. [DOI: 10.1134/s1061934822050070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract
We review the main techniques, procedures, and information products used in non-target analysis (NTA) to reveal the composition of substances. Sampling and sample preparation methods are preferable that ensure the extraction of analytes from test samples in a wide range of analyte properties with the most negligible loss. The necessary techniques of analysis are versions of chromatography–high-resolution tandem mass spectrometry (HRMS), yielding individual characteristics of analytes (mass spectra, retention properties) to accurately identify them. The prioritization of the analytical strategy discards unnecessary measurements and thereby increases the performance of the NTA. Chemical databases, collections of reference mass spectra and retention characteristics, algorithms, and software for processing HRMS data are indispensable in NTA.
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Popov RS, Ivanchina NV, Dmitrenok PS. Application of MS-Based Metabolomic Approaches in Analysis of Starfish and Sea Cucumber Bioactive Compounds. Mar Drugs 2022; 20:md20050320. [PMID: 35621972 PMCID: PMC9147407 DOI: 10.3390/md20050320] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/11/2022] [Accepted: 05/11/2022] [Indexed: 12/12/2022] Open
Abstract
Today, marine natural products are considered one of the main sources of compounds for drug development. Starfish and sea cucumbers are potential sources of natural products of pharmaceutical interest. Among their metabolites, polar steroids, triterpene glycosides, and polar lipids have attracted a great deal of attention; however, studying these compounds by conventional methods is challenging. The application of modern MS-based approaches can help to obtain valuable information about such compounds. This review provides an up-to-date overview of MS-based applications for starfish and sea cucumber bioactive compounds analysis. While describing most characteristic features of MS-based approaches in the context of starfish and sea cucumber metabolites, including sample preparation and MS analysis steps, the present paper mainly focuses on the application of MS-based metabolic profiling of polar steroid compounds, triterpene glycosides, and lipids. The application of MS in metabolomics studies is also outlined.
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Affiliation(s)
- Roman S. Popov
- Correspondence: (R.S.P.); (P.S.D.); Tel.: +7-423-231-1132 (P.S.D.)
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Zhong P, Wei X, Li X, Wei X, Wu S, Huang W, Koidis A, Xu Z, Lei H. Untargeted metabolomics by liquid chromatography‐mass spectrometry for food authentication: A review. Compr Rev Food Sci Food Saf 2022; 21:2455-2488. [DOI: 10.1111/1541-4337.12938] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 02/20/2022] [Accepted: 02/21/2022] [Indexed: 12/17/2022]
Affiliation(s)
- Peng Zhong
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiaoqun Wei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiangmei Li
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiaoyi Wei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Shaozong Wu
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Weijuan Huang
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Anastasios Koidis
- Institute for Global Food Security Queen's University Belfast Belfast UK
| | - Zhenlin Xu
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Hongtao Lei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
- Guangdong Laboratory for Lingnan Modern Agriculture South China Agricultural University Guangzhou 510642 China
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Hu H, Hu C, Peng J, Ghosh AK, Khan A, Sun D, Luyten W. Bioassay-Guided Interpretation of Antimicrobial Compounds in Kumu, a TCM Preparation From Picrasma quassioides' Stem via UHPLC-Orbitrap-Ion Trap Mass Spectrometry Combined With Fragmentation and Retention Time Calculation. Front Pharmacol 2021; 12:761751. [PMID: 34776978 PMCID: PMC8581800 DOI: 10.3389/fphar.2021.761751] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/16/2021] [Indexed: 12/03/2022] Open
Abstract
The stem of Picrasma quassioides (PQ) was recorded as a prominent traditional Chinese medicine, Kumu, which was effective for microbial infection, inflammation, fever, and dysentery, etc. At present, Kumu is widely used in China to develop different medicines, even as injection (Kumu zhusheye), for combating infections. However, the chemical basis of its antimicrobial activity has still not been elucidated. To examine the active chemicals, its stem was extracted to perform bioassay-guided purification against Staphylococcus aureus and Escherichia coli. In this study, two types of columns (normal and reverse-phase) were used for speedy bioassay-guided isolation from Kumu, and the active peaks were collected and identified via an UHPLC-Orbitrap-Ion Trap Mass Spectrometer, combined with MS Fragmenter and ChromGenius. For identification, the COCONUT Database (largest database of natural products) and a manually built PQ database were used, in combination with prediction and calculation of mass fragmentation and retention time to better infer their structures, especially for isomers. Moreover, three standards were analyzed under different conditions for developing and validating the MS method. A total of 25 active compounds were identified, including 24 alkaloids and 1 triterpenoid against S. aureus, whereas only β-carboline-1-carboxylic acid and picrasidine S were active against E. coli. Here, the good antimicrobial activity of 18 chemicals was reported for the first time. Furthermore, the spectrum of three abundant β-carbolines was assessed via their IC50 and MBC against various human pathogens. All of them exhibited strong antimicrobial activities with good potential to be developed as antibiotics. This study clearly showed the antimicrobial chemical basis of Kumu, and the results demonstrated that HRMS coupled with MS Fragmenter and ChromGenius was a powerful tool for compound analysis, which can be used for other complex samples. Beta-carbolines reported here are important lead compounds in antibiotic discovery.
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Affiliation(s)
- Haibo Hu
- Department of Biology, Animal Physiology and Neurobiology Section, KU Leuven, Leuven, Belgium.,National Engineering Research Center for Modernization of Traditional Chinese Medicine - Hakka Medical Resources Branch, School of Pharmacy, Gannan Medical University, Ganzhou, China
| | - Changling Hu
- Laboratory for Functional Foods and Human Health, Center for Excellence in Postharvest Technologies, North Carolina Agricultural and Technical State University, North Carolina Research Campus, Kannapolis, NC, United States
| | - Jinnian Peng
- National Engineering Research Center for Modernization of Traditional Chinese Medicine - Hakka Medical Resources Branch, School of Pharmacy, Gannan Medical University, Ganzhou, China
| | - Alokesh Kumar Ghosh
- Department of Biology, Animal Physiology and Neurobiology Section, KU Leuven, Leuven, Belgium
| | - Ajmal Khan
- Department of Biology, Animal Physiology and Neurobiology Section, KU Leuven, Leuven, Belgium
| | - Dan Sun
- Department of Biology, Animal Physiology and Neurobiology Section, KU Leuven, Leuven, Belgium.,College of Life Sciences, NanKai University, Tianjin, China
| | - Walter Luyten
- Department of Biology, Animal Physiology and Neurobiology Section, KU Leuven, Leuven, Belgium
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Maia M, Figueiredo A, Cordeiro C, Sousa Silva M. FT-ICR-MS-based metabolomics: A deep dive into plant metabolism. Mass Spectrom Rev 2021. [PMID: 34545595 DOI: 10.1002/mas.21731] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/30/2021] [Accepted: 09/09/2021] [Indexed: 06/13/2023]
Abstract
Metabolomics involves the identification and quantification of metabolites to unravel the chemical footprints behind cellular regulatory processes and to decipher metabolic networks, opening new insights to understand the correlation between genes and metabolites. In plants, it is estimated the existence of hundreds of thousands of metabolites and the majority is still unknown. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a powerful analytical technique to tackle such challenges. The resolving power and sensitivity of this ultrahigh mass accuracy mass analyzer is such that a complex mixture, such as plant extracts, can be analyzed and thousands of metabolite signals can be detected simultaneously and distinguished based on the naturally abundant elemental isotopes. In this review, FT-ICR-MS-based plant metabolomics studies are described, emphasizing FT-ICR-MS increasing applications in plant science through targeted and untargeted approaches, allowing for a better understanding of plant development, responses to biotic and abiotic stresses, and the discovery of new natural nutraceutical compounds. Improved metabolite extraction protocols compatible with FT-ICR-MS, metabolite analysis methods and metabolite identification platforms are also explored as well as new in silico approaches. Most recent advances in MS imaging are also discussed.
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Affiliation(s)
- Marisa Maia
- Departamento de Química e Bioquímica, Laboratório de FTICR e Espectrometria de Massa Estrutural, MARE-Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
- Departamento de Biologia Vegetal, Faculdade de Ciências, Grapevine Pathogen Systems Lab (GPS Lab), Biosystems and Integrative Sciences Institute (BioISI), Universidade de Lisboa, Lisboa, Portugal
| | - Andreia Figueiredo
- Departamento de Biologia Vegetal, Faculdade de Ciências, Grapevine Pathogen Systems Lab (GPS Lab), Biosystems and Integrative Sciences Institute (BioISI), Universidade de Lisboa, Lisboa, Portugal
| | - Carlos Cordeiro
- Departamento de Química e Bioquímica, Laboratório de FTICR e Espectrometria de Massa Estrutural, MARE-Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Marta Sousa Silva
- Departamento de Química e Bioquímica, Laboratório de FTICR e Espectrometria de Massa Estrutural, MARE-Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
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