1
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Bui-Thi D, Liu Y, Lippens JL, Laukens K, De Vijlder T. TransExION: a transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry. J Cheminform 2024; 16:61. [PMID: 38807166 PMCID: PMC11134763 DOI: 10.1186/s13321-024-00858-5] [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: 12/22/2023] [Accepted: 05/12/2024] [Indexed: 05/30/2024] Open
Abstract
Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. SCIENTIFIC CONTRIBUTION: We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.
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Affiliation(s)
- Danh Bui-Thi
- Computer Science Department, University of Antwerp, Middelheimlaan 1, 2020, Antwerp, Belgium
| | - Youzhong Liu
- Therapeutic Development and Supply, Janssen Pharmaceutica N.V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Jennifer L Lippens
- Therapeutic Development and Supply, Janssen Pharmaceutica N.V., Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Kris Laukens
- Computer Science Department, University of Antwerp, Middelheimlaan 1, 2020, Antwerp, Belgium
| | - Thomas De Vijlder
- Therapeutic Development and Supply, Janssen Pharmaceutica N.V., Turnhoutseweg 30, 2340, Beerse, Belgium.
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2
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Partington JM, Rana S, Szabo D, Anumol T, Clarke BO. Comparison of high-resolution mass spectrometry acquisition methods for the simultaneous quantification and identification of per- and polyfluoroalkyl substances (PFAS). Anal Bioanal Chem 2024; 416:895-912. [PMID: 38159142 DOI: 10.1007/s00216-023-05075-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 11/02/2023] [Accepted: 11/23/2023] [Indexed: 01/03/2024]
Abstract
Simultaneous identification and quantification of per- and polyfluoroalkyl substances (PFAS) were evaluated for three quadrupole time-of-flight mass spectrometry (QTOF) acquisition methods. The acquisition methods investigated were MS-Only, all ion fragmentation (All-Ions), and automated tandem mass spectrometry (Auto-MS/MS). Target analytes were the 25 PFAS of US EPA Method 533 and the acquisition methods were evaluated by analyte response, limit of quantification (LOQ), accuracy, precision, and target-suspect screening identification limit (IL). PFAS LOQs were consistent across acquisition methods, with individual PFAS LOQs within an order of magnitude. The mean and range for MS-Only, All-Ions, and Auto-MS/MS are 1.3 (0.34-5.1), 2.1 (0.49-5.1), and 1.5 (0.20-5.1) pg on column. For fast data processing and tentative identification with lower confidence, MS-Only is recommended; however, this can lead to false-positives. Where high-confidence identification, structural characterisation, and quantification are desired, Auto-MS/MS is recommended; however, cycle time should be considered where many compounds are anticipated to be present. For comprehensive screening workflows and sample archiving, All-Ions is recommended, facilitating both quantification and retrospective analysis. This study validated HRMS acquisition approaches for quantification (based upon precursor data) and exploration of identification workflows for a range of PFAS compounds.
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Affiliation(s)
- Jordan M Partington
- Australian Laboratory for Emerging Contaminants, School of Chemistry, University of Melbourne, Victoria, 3010, Australia
| | - Sahil Rana
- Australian Laboratory for Emerging Contaminants, School of Chemistry, University of Melbourne, Victoria, 3010, Australia
| | - Drew Szabo
- Australian Laboratory for Emerging Contaminants, School of Chemistry, University of Melbourne, Victoria, 3010, Australia
- Department of Materials and Environmental Chemistry, Stockholm University, 11418, Stockholm, Sweden
| | - Tarun Anumol
- Agilent Technologies Inc, Wilmington, DE, 19808, USA
| | - Bradley O Clarke
- Australian Laboratory for Emerging Contaminants, School of Chemistry, University of Melbourne, Victoria, 3010, Australia.
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3
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Karapakdee P, Wilairat P, Kokpol S, Nolvachai Y, Kulsing C. Data independent acquisition for gas chromatographic MS/MS analysis of volatile compounds. J Chromatogr A 2024; 1714:464527. [PMID: 38056391 DOI: 10.1016/j.chroma.2023.464527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/18/2023] [Accepted: 11/21/2023] [Indexed: 12/08/2023]
Abstract
This study presents a novel tandem mass spectrometry (MS/MS) approach utilizing a data independent acquisition (DIA) concept specifically designed with gas chromatography-electron ionization-triple quadrupole mass spectrometry (GC-EI-QqQMS). This allows compound identification based on comparison between all the experimental MS/MS product ion spectra and the simulated library data of >1,000 MS/MS transitions of 71 compounds. The simulation data were generated by using the Competitive Fragmentation Modeling (CFM-ID) 3.0 program. The approach for calculation of the DIA MS/MS library match scores was then established and applied for identification of a range of terpenoids and oxygenated compounds in perfume. The identity of each peak was confirmed using 4-241 MS/MS transitions. The established data collection and analysis methods are expected to be useful for increased confidence in volatile compound analysis.
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Affiliation(s)
- Premkamol Karapakdee
- Department of Chemistry, Faculty of Science, Chulalongkorn University, 254 Phyathai Road, Wangmai, Pathumwan, Bangkok 10330, Thailand
| | - Prapin Wilairat
- Flow Innovation-Research for Science and Technology Laboratories (Firstlabs), Ratchathewi District, Bangkok 10110, Thailand; Department of Chemistry and Center of Excellence for Innovation in Chemistry, Faculty of Science, Mahidol University, Rama 6 Road, Ratchathewi District, Bangkok 10400, Thailand
| | - Sirirat Kokpol
- Department of Chemistry, Faculty of Science, Chulalongkorn University, 254 Phyathai Road, Wangmai, Pathumwan, Bangkok 10330, Thailand; Food Research and Testing Laboratory, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
| | - Yada Nolvachai
- CASS Food Research Centre, School of Exercise and Nutritional Sciences, Faculty of Health, Deakin University, Burwood 3125, Victoria, Australia
| | - Chadin Kulsing
- Department of Chemistry, Faculty of Science, Chulalongkorn University, 254 Phyathai Road, Wangmai, Pathumwan, Bangkok 10330, Thailand; Metabolomics for Life Sciences Research Unit, Chulalongkorn University, Bangkok 10330, Thailand.
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4
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Hong S, Lee J, Cha J, Gwak J, Khim JS. Effect-Directed Analysis Combined with Nontarget Screening to Identify Unmonitored Toxic Substances in the Environment. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19148-19155. [PMID: 37972298 DOI: 10.1021/acs.est.3c05035] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Effect-directed analysis (EDA) combined with nontarget screening (NTS) has established a valuable tool for the identification of unmonitored toxic substances in environmental samples. It consists of three main steps: (1) highly potent fraction identification, (2) toxicant candidate selection, and (3) major toxicant identification. Here, we discuss the methodology, current status, limitations, and future challenges of EDA combined with NTS. This method has been applied successfully to various environmental samples, such as sediments, wastewater treatment plant effluents, and biota. We present several case studies and highlight key results. EDA has undergone significant technological advancements in the past 20 years, with the establishment of its key components: target chemical analysis, bioassays, fractionation, NTS, and data processing. However, it has not been incorporated widely into environmental monitoring programs. We provide suggestions for the application of EDA combined with NTS in environmental monitoring programs and management, with the identification of further research needs.
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Affiliation(s)
- Seongjin Hong
- Department of Marine Environmental Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Junghyun Lee
- School of Earth and Environmental Sciences & Research Institute of Oceanography, Seoul National University, Seoul 08826, Republic of Korea
- Department of Environmental Education, Kongju National University, Gongju 32588, Republic of Korea
| | - Jihyun Cha
- Department of Marine Environmental Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Jiyun Gwak
- Department of Marine Environmental Science, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Jong Seong Khim
- School of Earth and Environmental Sciences & Research Institute of Oceanography, Seoul National University, Seoul 08826, Republic of Korea
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5
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Anwardeen NR, Diboun I, Mokrab Y, Althani AA, Elrayess MA. Statistical methods and resources for biomarker discovery using metabolomics. BMC Bioinformatics 2023; 24:250. [PMID: 37322419 DOI: 10.1186/s12859-023-05383-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 06/09/2023] [Indexed: 06/17/2023] Open
Abstract
Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.
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Affiliation(s)
- Najeha R Anwardeen
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Ilhame Diboun
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Younes Mokrab
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Asma A Althani
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
- QU Health, Qatar University, Doha, Qatar
| | - Mohamed A Elrayess
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar.
- QU Health, Qatar University, Doha, Qatar.
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6
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Gaudêncio SP, Bayram E, Lukić Bilela L, Cueto M, Díaz-Marrero AR, Haznedaroglu BZ, Jimenez C, Mandalakis M, Pereira F, Reyes F, Tasdemir D. Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar Drugs 2023; 21:md21050308. [PMID: 37233502 DOI: 10.3390/md21050308] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.
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Affiliation(s)
- Susana P Gaudêncio
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
- UCIBIO-Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Engin Bayram
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Lada Lukić Bilela
- Department of Biology, Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Mercedes Cueto
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
| | - Ana R Díaz-Marrero
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
- Instituto Universitario de Bio-Orgánica (IUBO), Universidad de La Laguna, 38206 La Laguna, Spain
| | - Berat Z Haznedaroglu
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Carlos Jimenez
- CICA- Centro Interdisciplinar de Química e Bioloxía, Departamento de Química, Facultade de Ciencias, Universidade da Coruña, 15071 A Coruña, Spain
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, HCMR Thalassocosmos, 71500 Gournes, Crete, Greece
| | - Florbela Pereira
- LAQV, REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Fernando Reyes
- Fundación MEDINA, Avda. del Conocimiento 34, 18016 Armilla, Spain
| | - Deniz Tasdemir
- GEOMAR Centre for Marine Biotechnology (GEOMAR-Biotech), Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Am Kiel-Kanal 44, 24106 Kiel, Germany
- Faculty of Mathematics and Natural Science, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
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7
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Corinti D, Rotari L, Crestoni ME, Fornarini S, Oomens J, Berden G, Tintaru A, Chiavarino B. Protonated Forms of Naringenin and Naringenin Chalcone: Proteiform Bioactive Species Elucidated by IRMPD Spectroscopy, IMS, CID-MS, and Computational Approaches. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:4005-4015. [PMID: 36849438 PMCID: PMC9999425 DOI: 10.1021/acs.jafc.2c07453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/08/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Naringenin (Nar) and its structural isomer, naringenin chalcone (ChNar), are two natural phytophenols with beneficial health effects belonging to the flavonoids family. A direct discrimination and structural characterization of the protonated forms of Nar and ChNar, delivered into the gas phase by electrospray ionization (ESI), was performed by mass spectrometry-based methods. In this study, we exploit a combination of electrospray ionization coupled to (high-resolution) mass spectrometry (HR-MS), collision-induced dissociation (CID) measurements, IR multiple-photon dissociation (IRMPD) action spectroscopy, density functional theory (DFT) calculations, and ion mobility-mass spectrometry (IMS). While IMS and variable collision-energy CID experiments hardly differentiate the two isomers, IRMPD spectroscopy appears to be an efficient method to distinguish naringenin from its related chalcone. In particular, the spectral range between 1400 and 1700 cm-1 is highly specific in discriminating between the two protonated isomers. Selected vibrational signatures in the IRMPD spectra have allowed us to identify the nature of the metabolite present in methanolic extracts of commercial tomatoes and grapefruits. Furthermore, comparisons between experimental IRMPD and calculated IR spectra have clarified the geometries adopted by the two protonated isomers, allowing a conformational analysis of the probed species.
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Affiliation(s)
- Davide Corinti
- Dipartimento
di Chimica e Tecnologie del Farmaco, Sapienza
Università di Roma, Piazzale Aldo Moro 5, 00185 Roma, Italy
| | - Lucretia Rotari
- Dipartimento
di Chimica e Tecnologie del Farmaco, Sapienza
Università di Roma, Piazzale Aldo Moro 5, 00185 Roma, Italy
| | - Maria Elisa Crestoni
- Dipartimento
di Chimica e Tecnologie del Farmaco, Sapienza
Università di Roma, Piazzale Aldo Moro 5, 00185 Roma, Italy
| | - Simonetta Fornarini
- Dipartimento
di Chimica e Tecnologie del Farmaco, Sapienza
Università di Roma, Piazzale Aldo Moro 5, 00185 Roma, Italy
| | - Jos Oomens
- FELIX
Laboratory, Institute for Molecules and Materials, Radboud University, Toernooiveld 7, Nijmegen 6525ED, Netherlands
| | - Giel Berden
- FELIX
Laboratory, Institute for Molecules and Materials, Radboud University, Toernooiveld 7, Nijmegen 6525ED, Netherlands
| | - Aura Tintaru
- CNRS,
Centre Interdisciplinaire de Nanoscience de Marseille, CINaM UMR 7325, Aix Marseille University, Marseille 13288, France
| | - Barbara Chiavarino
- Dipartimento
di Chimica e Tecnologie del Farmaco, Sapienza
Università di Roma, Piazzale Aldo Moro 5, 00185 Roma, Italy
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8
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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: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [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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Affiliation(s)
| | | | | | - Sebastian Böcker
- Chair for Bioinformatics, Institute for Computer Science, Friedrich-Schiller-University Jena, 07743 Jena, Germany
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9
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Morehouse NJ, Clark TN, McMann EJ, van Santen JA, Haeckl FPJ, Gray CA, Linington RG. Annotation of natural product compound families using molecular networking topology and structural similarity fingerprinting. Nat Commun 2023; 14:308. [PMID: 36658161 PMCID: PMC9852437 DOI: 10.1038/s41467-022-35734-z] [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: 11/05/2021] [Accepted: 12/20/2022] [Indexed: 01/20/2023] Open
Abstract
Spectral matching of MS2 fragmentation spectra has become a popular method for characterizing natural products libraries but identification remains challenging due to differences in MS2 fragmentation properties between instruments and the low coverage of current spectral reference libraries. To address this bottleneck we present Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) which matches chemical similarity grouping in the Natural Products Atlas to grouping of mass spectrometry features from molecular networking. This approach assigns compound families to molecular networking subnetworks without the need for experimental or calculated reference spectra. We demonstrate SNAP-MS can accurately annotate subnetworks built from both reference spectra and an in-house microbial extract library, and correctly predict compound families from published molecular networks acquired on a range of MS instrumentation. Compound family annotations for the microbial extract library are validated by co-injection of standards or isolation and spectroscopic analysis. SNAP-MS is freely available at www.npatlas.org/discover/snapms .
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Affiliation(s)
- Nicholas J Morehouse
- Department of Biological Sciences, University of New Brunswick, Saint John, NB, Canada
| | - Trevor N Clark
- Department of Chemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Emily J McMann
- Department of Chemistry, Simon Fraser University, Burnaby, BC, Canada
| | | | - F P Jake Haeckl
- Department of Chemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Christopher A Gray
- Department of Biological Sciences, University of New Brunswick, Saint John, NB, Canada.,Department of Chemistry, University of New Brunswick, Fredericton, NB, Canada
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, Burnaby, BC, Canada.
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10
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Mass Spectrometric Methods for Non-Targeted Screening of Metabolites: A Future Perspective for the Identification of Unknown Compounds in Plant Extracts. SEPARATIONS 2022. [DOI: 10.3390/separations9120415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. This review aims to provide an overview of various techniques and tools for non-target screening of phyto samples using LC-HRMS/MS.
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11
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Comparative Analysis of Binary Similarity Measures for Compound Identification in MassSpectrometry-Based Metabolomics. Metabolites 2022; 12:metabo12080694. [PMID: 35893261 PMCID: PMC9394311 DOI: 10.3390/metabo12080694] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 02/01/2023] Open
Abstract
Compound identification is a critical step in untargeted metabolomics. Its most important procedure is to calculate the similarity between experimental mass spectra and either predicted mass spectra or mass spectra in a mass spectral library. Unlike the continuous similarity measures, there is no study to assess the performance of binary similarity measures in compound identification, even though the well-known Jaccard similarity measure has been widely used without proper evaluation. The objective of this study is thus to evaluate the performance of binary similarity measures for compound identification in untargeted metabolomics. Fifteen binary similarity measures, including the well-known Jaccard, Dice, Sokal–Sneath, Cosine, and Simpson measures, were selected to assess their performance in compound identification. using both electron ionization (EI) and electrospray ionization (ESI) mass spectra. Our theoretical evaluations show that the accuracy of the compound identification was exactly the same between the Jaccard, Dice, 3W-Jaccard, Sokal–Sneath, and Kulczynski measures, between the Cosine and Hellinger measures, and between the McConnaughey and Driver–Kroeber measures, which were practically confirmed using mass spectra libraries. From the mass spectrum-based evaluation, we observed that the best performing similarity measures were the McConnaughey and Driver–Kroeber measures for EI mass spectra and the Cosine and Hellinger measures for ESI mass spectra. The most robust similarity measure was the Fager–McGowan measure, the second-best performing similarity measure in both EI and ESI mass spectra.
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12
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Yan S, Bhawal R, Yin Z, Thannhauser TW, Zhang S. Recent advances in proteomics and metabolomics in plants. MOLECULAR HORTICULTURE 2022; 2:17. [PMID: 37789425 PMCID: PMC10514990 DOI: 10.1186/s43897-022-00038-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/20/2022] [Indexed: 10/05/2023]
Abstract
Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.
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Affiliation(s)
- Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Ruchika Bhawal
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA
| | - Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | | | - Sheng Zhang
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA.
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13
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Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
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14
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Zhou L, Yu D, Zheng S, Ouyang R, Wang Y, Xu G. Gut microbiota-related metabolome analysis based on chromatography-mass spectrometry. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116375] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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15
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Li P, Shen J, Li Y, Yao H, Yu M, He C, Xiao P. Metabolite Profiling Based on UPLC-Q-TOF-MS/MS and the Biological Evaluation of Medicinal Plants of Chinese Dichocarpum (Ranunculaceae). Chem Biodivers 2021; 18:e2100432. [PMID: 34351062 DOI: 10.1002/cbdv.202100432] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/05/2021] [Indexed: 12/16/2022]
Abstract
The genus Dichocarpum is endemic to East Asia, and many species have been used to treat various diseases. However, phytochemical researches of this genus have been limited to date. In the present study, a metabolomic approach based on UPLC-Q-TOF-MS/MS was used to explore the phytochemical profiles of 10 Chinese Dichocarpum species, and cannabinoid receptor (CB1/CB2) agonistic activities evaluation of these plants was performed. A total of 128 features were putatively annotated, belonging to alkaloids, flavonoids, triterpenes saponins, phenolic acids, and others. Semi-quantitative statistics demonstrated that alkaloids and flavonoids were widely distributed, with the former the most abundant, whereas triterpenes saponins were mainly distributed in D. fargesii and D. wuchuanense. The phylogenetic results obtained from DNA sequencing assigned the 10 species to three groups. Further results of in silico annotation revealed three chemical families and helped determine the characteristic features of the three groups. In addition, the plant extracts of nine species from this genus showed agonistic activity on CB2 receptors. This comprehensive analysis revealed the chemotype distribution and pharmacophylogenetic relationship, to provide clues for the prospective resource utilization of the medicinal plants from the genus Dichocarpum.
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Affiliation(s)
- Pei Li
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100193, P. R. China.,Key Laboratory of Bioactive Substances, Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, 100193, P. R. China
| | - Jie Shen
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100193, P. R. China.,Key Laboratory of Bioactive Substances, Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, 100193, P. R. China
| | - Yue Li
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100193, P. R. China.,Key Laboratory of Bioactive Substances, Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, 100193, P. R. China
| | - Hui Yao
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100193, P. R. China
| | - Meng Yu
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100193, P. R. China
| | - Chunnian He
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100193, P. R. China.,Key Laboratory of Bioactive Substances, Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, 100193, P. R. China
| | - Peigen Xiao
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100193, P. R. China.,Key Laboratory of Bioactive Substances, Resources Utilization of Chinese Herbal Medicine, Ministry of Education, Beijing, 100193, P. R. China
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16
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Krettler CA, Thallinger GG. A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics. Brief Bioinform 2021; 22:6184408. [PMID: 33758925 DOI: 10.1093/bib/bbab073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/29/2021] [Accepted: 02/12/2021] [Indexed: 12/27/2022] Open
Abstract
Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.
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Affiliation(s)
- Christoph A Krettler
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
| | - Gerhard G Thallinger
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
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17
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Mass spectrometry based untargeted metabolomics for plant systems biology. Emerg Top Life Sci 2021; 5:189-201. [DOI: 10.1042/etls20200271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/04/2021] [Accepted: 02/22/2021] [Indexed: 12/12/2022]
Abstract
Untargeted metabolomics enables the identification of key changes to standard pathways, but also aids in revealing other important and possibly novel metabolites or pathways for further analysis. Much progress has been made in this field over the past decade and yet plant metabolomics seems to still be an emerging approach because of the high complexity of plant metabolites and the number one challenge of untargeted metabolomics, metabolite identification. This final and critical stage remains the focus of current research. The intention of this review is to give a brief current state of LC–MS based untargeted metabolomics approaches for plant specific samples and to review the emerging solutions in mass spectrometer hardware and computational tools that can help predict a compound's molecular structure to improve the identification rate.
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18
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Dickson L, Tenon M, Svilar L, Fança-Berthon P, Martin JC, Rogez H, Vaillant F. Genipap (Genipa americana L.) juice intake biomarkers after medium-term consumption. Food Res Int 2020; 137:109375. [PMID: 33233077 DOI: 10.1016/j.foodres.2020.109375] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 05/26/2020] [Accepted: 06/02/2020] [Indexed: 11/30/2022]
Abstract
Genipap (Genipa americana L.) is an exotic fruit largely consumed and well known, in Amazonian pharmacopeia, to treat anemia, measles and uterine cancer. It is also used as a diuretic, digestive, healing, laxative and antiseptic. The aim of this study was to apply an untargeted metabolomics strategy to determine biomarkers of food intake after short-term consumption of genipap juice. Sixteen healthy adult men were administered jenipap juice (250 mL) twice a day for three weeks. Before and after the three weeks of consumption. the subjects drank a control drink, and they consumed a standard diet. Urine was collected after 0-6 h, 6-12 h and 12-24 h. An ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS)-based metabolomics approach was applied to analyze the urine samples. Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed to highlight experimental differences between groups. The value of the area under the curve (AUC) of the receiver operator characteristic (ROC) curve validated the identified biomarkers. Thirty-one statistically affected urinary metabolites were putatively identified and were mainly related to iridoids family, medium-chain fatty acids, and polyphenols. Also a group of urinary markers including dihydrocaffeic acid (DHCA), 1-(4-hydroxyphenyl)-1,2-propanediol and 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid were established as biomarkers of genipap consumption. Our findings have established a comprehensive panel of changes in the urinary metabolome and provided information to monitor endogenous alterations that are linked to genipap juice intake. These data should be used in further studies to understand the health implications of genipap juice consumption.
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Affiliation(s)
- Livia Dickson
- Federal University of Pará & Centre for Valorization of Amazonian Bioactive Compounds (CVACBA), Parque de Ciência e Tecnologia Guamá, Av. Perimetral da Ciência, km 01, Guamá 66075-750, Brazil; Naturex SA, 250 rue Pierre Bayle, BP81218, 84911 Avignon CEDEX 9, France; Centre International de Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, TA50/PS4, 34398 Montpellier CEDEX 5, France; Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France
| | - Mathieu Tenon
- Naturex SA, 250 rue Pierre Bayle, BP81218, 84911 Avignon CEDEX 9, France.
| | - Ljubica Svilar
- Aix Marseille Univ, INSERM, INRA, C2VN, CRIBIOM, 5-9, Boulevard Maurice Bourdet, CS 80501, 13205 Marseille CEDEX 01, France.
| | | | - Jean-Charles Martin
- Aix Marseille Univ, INSERM, INRA, C2VN, CRIBIOM, 5-9, Boulevard Maurice Bourdet, CS 80501, 13205 Marseille CEDEX 01, France.
| | - Hervé Rogez
- Federal University of Pará & Centre for Valorization of Amazonian Bioactive Compounds (CVACBA), Parque de Ciência e Tecnologia Guamá, Av. Perimetral da Ciência, km 01, Guamá 66075-750, Brazil
| | - Fabrice Vaillant
- Centre International de Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, TA50/PS4, 34398 Montpellier CEDEX 5, France; Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France.
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19
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Wu X, Li X, Wang W, Shan Y, Wang C, Zhu M, La Q, Zhong Y, Xu Y, Nan P, Li X. Integrated metabolomics and transcriptomics study of traditional herb Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao reveals global metabolic profile and novel phytochemical ingredients. BMC Genomics 2020; 21:697. [PMID: 33208098 PMCID: PMC7677826 DOI: 10.1186/s12864-020-07005-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao is one of the most common herbs widely used in South and East Asia, to enhance people's health and reinforce vital energy. Despite its prevalence, however, the knowledge about phytochemical compositions and metabolite biosynthesis in Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao is very limited. RESULTS An integrated metabolomics and transcriptomics analysis using state-of-the-art UPLC-Q-Orbitrap mass spectrometer and advanced bioinformatics pipeline were conducted to study global metabolic profiles and phytochemical ingredients/biosynthesis in Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao. A total of 5435 metabolites were detected, from which 2190 were annotated, representing an order of magnitude increase over previously known. Metabolic profiling of Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao tissues found contents and synthetic enzymes for phytochemicals were significantly higher in leaf and stem in general, whereas the contents of the main bioactive ingredients were significantly enriched in root, underlying the value of root in herbal remedies. Using integrated metabolomics and transcriptomics data, we illustrated the complete pathways of phenylpropanoid biosynthesis, flavonoid biosynthesis, and isoflavonoid biosynthesis, in which some were first reported in the herb. More importantly, we discovered novel flavonoid derivatives using informatics method for neutral loss scan, in addition to inferring their likely synthesis pathways in Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao. CONCLUSIONS The current study represents the most comprehensive metabolomics and transcriptomics analysis on traditional herb Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao. We demonstrated our integrated metabolomics and transcriptomics approach offers great potentials in discovering novel metabolite structure and associated synthesis pathways. This study provides novel insights into the phytochemical ingredients, metabolite biosynthesis, and complex metabolic network in herbs, highlighting the rich natural resource and nutritional value of traditional herbal plants.
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Affiliation(s)
- Xueting Wu
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Xuetong Li
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wei Wang
- Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Yuanhong Shan
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Cuiting Wang
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
| | - Mulan Zhu
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
- Shanghai Chenshan Plant Science Research Center, Shanghai Chenshan Botanical Garden, Shanghai, 201602, China
| | - Qiong La
- Research Institute of Biodiversity & Geobiology, Department of Life Science, Tibet University, Lhasa, China 850000, China
| | - Yang Zhong
- Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
- Research Institute of Biodiversity & Geobiology, Department of Life Science, Tibet University, Lhasa, China 850000, China
| | - Ye Xu
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Peng Nan
- Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China.
| | - Xuan Li
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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20
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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: 4.5] [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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21
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Mass spectrometry-based metabolomics for an in-depth questioning of human health. Adv Clin Chem 2020; 99:147-191. [PMID: 32951636 DOI: 10.1016/bs.acc.2020.02.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Today, metabolomics is becoming an indispensable tool to get a more comprehensive analysis of complex living systems, providing insights on multiple aspects of physiology. Although its application in large scale population-based studies is very challenging due to the processing of large sample sets as well as the complexity of data information, its potential to characterize human health is well recognized. Technological advances in metabolomics pave the way for the efficient biomarker discovery of disease etiology, diagnosis and prognosis. Here, different steps of the metabolomics workflow, particularly mass spectrometry-based approaches, are discussed to demonstrate the potential of metabolomics to address biological questioning in human health. First an overview of metabolomics is provided with its interest in human health studies. Analytical development and advances in mass spectrometry instrumentation and computational tools are discussed regarding their application limits. Advancing metabolomics for applicability in human health and large-scale studies is presented and discussed in conclusion.
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22
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Liu FJ, Jiang Y, Li P, Liu YD, Xin GZ, Yao ZP, Li HJ. Diagnostic fragmentation-assisted mass spectral networking coupled with in silico dereplication for deep annotation of steroidal alkaloids in medicinal Fritillariae Bulbus. JOURNAL OF MASS SPECTROMETRY : JMS 2020; 55:e4528. [PMID: 32559823 DOI: 10.1002/jms.4528] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 04/03/2020] [Accepted: 04/07/2020] [Indexed: 06/11/2023]
Abstract
Fully understanding the chemicals in an herbal medicine remains a challenging task. Molecular networking (MN) allows to organize tandem mass spectrometry (MS/MS) data in complex samples by mass spectral similarity, which yet suffers from low coverage and accuracy of compound annotation due to the size limitation of available databases and differentiation obstacle of similar chemical scaffolds. In this work, an enhanced MN-based strategy named diagnostic fragmentation-assisted molecular networking coupled with in silico dereplication (DFMN-ISD) was introduced to overcome these obstacles: the rule-based fragmentation patterns provide insights into similar chemical scaffolds, the generated in silico candidates based on metabolic reactions expand the available natural product databases, and the in silico annotation method facilitates the further dereplication of candidates by computing their fragmentation trees. As a case, this approach was applied to globally profile the steroidal alkaloids in Fritillariae bulbus, a commonly used antitussive and expectorant herbal medicine. Consequently, a total of 325 steroidal alkaloids were discovered, including 106 cis-D/E-cevanines, 142 trans-D/E-cevanines, 29 jervines, 23 veratramines, and 25 verazines. And 10 of them were confirmed by available reference standards. Approximately 70% of the putative steroidal alkaloids have never been reported in previous publications, demonstrating the benefit of DFMN-ISD approach for the comprehensive characterization of chemicals in a complex plant organism.
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Affiliation(s)
- Feng-Jie Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 210009, China
| | - Yan Jiang
- College of Chemical Engineering, Nanjing Forestry University, Nanjing, China
| | - Ping Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 210009, China
| | - Yang-Dan Liu
- School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
| | - Gui-Zhong Xin
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 210009, China
| | - Zhong-Ping Yao
- State Key Laboratory of Chemical Biology and Drug Discovery, Food Safety and Technology Research Centre and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Kowloon, China
- State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation) and Shenzhen Key Laboratory of Food Biological Safety Control, Shenzhen Research Institute of Hong Kong Polytechnic University, Shenzhen, China
| | - Hui-Jun Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 210009, China
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23
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Feature-based molecular networking in the GNPS analysis environment. Nat Methods 2020; 17:905-908. [PMID: 32839597 PMCID: PMC7885687 DOI: 10.1038/s41592-020-0933-6] [Citation(s) in RCA: 606] [Impact Index Per Article: 151.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 07/22/2020] [Indexed: 12/13/2022]
Abstract
Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present Feature-Based Molecular Networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. The FBMN method brings quantitative analyses, isomeric resolution, including from ion-mobility spectrometry, into molecular networks.
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Perez De Souza L, Alseekh S, Brotman Y, Fernie AR. Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation. Expert Rev Proteomics 2020; 17:243-255. [PMID: 32380880 DOI: 10.1080/14789450.2020.1766975] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
INTRODUCTION Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system. AREAS COVERED This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches. EXPERT OPINION Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.
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Affiliation(s)
- Leonardo Perez De Souza
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany
| | - Saleh Alseekh
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany.,Department of plant metabolomics, Centre of Plant Systems Biology and Biotechnology , Plovdiv, Bulgaria
| | - Yariv Brotman
- Department of Life Sciences, Ben-Gurion University of the Negev , Beersheba, Israel
| | - Alisdair R Fernie
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany.,Department of plant metabolomics, Centre of Plant Systems Biology and Biotechnology , Plovdiv, Bulgaria
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25
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Aron AT, Gentry EC, McPhail KL, Nothias LF, Nothias-Esposito M, Bouslimani A, Petras D, Gauglitz JM, Sikora N, Vargas F, van der Hooft JJJ, Ernst M, Kang KB, Aceves CM, Caraballo-Rodríguez AM, Koester I, Weldon KC, Bertrand S, Roullier C, Sun K, Tehan RM, Boya P CA, Christian MH, Gutiérrez M, Ulloa AM, Tejeda Mora JA, Mojica-Flores R, Lakey-Beitia J, Vásquez-Chaves V, Zhang Y, Calderón AI, Tayler N, Keyzers RA, Tugizimana F, Ndlovu N, Aksenov AA, Jarmusch AK, Schmid R, Truman AW, Bandeira N, Wang M, Dorrestein PC. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat Protoc 2020; 15:1954-1991. [PMID: 32405051 DOI: 10.1038/s41596-020-0317-5] [Citation(s) in RCA: 303] [Impact Index Per Article: 75.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 03/03/2020] [Indexed: 02/06/2023]
Abstract
Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.
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Affiliation(s)
- Allegra T Aron
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Emily C Gentry
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Kerry L McPhail
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Louis-Félix Nothias
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Mélissa Nothias-Esposito
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Amina Bouslimani
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Daniel Petras
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Julia M Gauglitz
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Nicole Sikora
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Fernando Vargas
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | | | - Madeleine Ernst
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Kyo Bin Kang
- College of Pharmacy, Sookmyung Women's University, Seoul, Korea
| | - Christine M Aceves
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | | | - Irina Koester
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Kelly C Weldon
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Center of Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Samuel Bertrand
- Groupe Mer, Molécules, Santé-EA 2160, UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes, Nantes, France
- ThalassOMICS Metabolomics Facility, Plateforme Corsaire, Biogenouest, Nantes, France
| | - Catherine Roullier
- College of Pharmacy, Sookmyung Women's University, Seoul, Korea
- ThalassOMICS Metabolomics Facility, Plateforme Corsaire, Biogenouest, Nantes, France
| | - Kunyang Sun
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Richard M Tehan
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR, USA
| | - Cristopher A Boya P
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City, Panama
- Department of Biotechnology, Acharya Nagarjuna University, Guntur, Nagarjuna Nagar, India
| | - Martin H Christian
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City, Panama
| | - Marcelino Gutiérrez
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City, Panama
| | | | | | - Randy Mojica-Flores
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City, Panama
- Departamento de Química, Universidad Autónoma de Chiriquí (UNACHI), David, Chiriquí, Panama
| | - Johant Lakey-Beitia
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City, Panama
| | - Victor Vásquez-Chaves
- Centro de Investigaciones en Productos Naturales (CIPRONA), Universidad de Costa Rica, San José, Costa Rica
| | - Yilue Zhang
- Department of Drug Discovery and Development, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA
| | - Angela I Calderón
- Department of Drug Discovery and Development, Harrison School of Pharmacy, Auburn University, Auburn, AL, USA
| | - Nicole Tayler
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City, Panama
- Department of Biotechnology, Acharya Nagarjuna University, Guntur, Nagarjuna Nagar, India
| | - Robert A Keyzers
- School of Chemical & Physical Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Fidele Tugizimana
- Centre for Plant Metabolomics Research, Department of Biochemistry, University of Johannesburg, Auckland Park, South Africa
- International R&D Division, Omnia Group (Pty) Ltd., Johannesburg, South Africa
| | - Nombuso Ndlovu
- Centre for Plant Metabolomics Research, Department of Biochemistry, University of Johannesburg, Auckland Park, South Africa
| | - Alexander A Aksenov
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Alan K Jarmusch
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Robin Schmid
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Andrew W Truman
- Department of Molecular Microbiology, John Innes Centre, Norwich, UK
| | - Nuno Bandeira
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.
| | - Mingxun Wang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
- Center for Computational Mass Spectrometry, University of California, San Diego, La Jolla, CA, USA.
- Department of Pharmacology, University of California, San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
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Two apples a day modulate human:microbiome co-metabolic processing of polyphenols, tyrosine and tryptophan. Eur J Nutr 2020; 59:3691-3714. [PMID: 32103319 DOI: 10.1007/s00394-020-02201-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 02/06/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE Validated biomarkers of food intake (BFIs) have recently been suggested as a useful tool to assess adherence to dietary guidelines or compliance in human dietary interventions. Although many new candidate biomarkers have emerged in the last decades for different foods from metabolic profiling studies, the number of comprehensively validated biomarkers of food intake is limited. Apples are among the most frequently consumed fruits and a rich source of polyphenols and fibers, an important mediator for their health-protective properties. METHODS Using an untargeted metabolomics approach, we aimed to identify biomarkers of long-term apple intake and explore how apples impact on the human plasma and urine metabolite profiles. Forty mildly hypercholesterolemic volunteers consumed two whole apples or a sugar and energy-matched control beverage, daily for 8 weeks in a randomized, controlled, crossover intervention study. The metabolome in plasma and urine samples was analyzed via untargeted metabolomics. RESULTS We found 61 urine and 9 plasma metabolites being statistically significant after the whole apple intake compared to the control beverage, including several polyphenol metabolites that could be used as BFIs. Furthermore, we identified several endogenous indole and phenylacetyl-glutamine microbial metabolites significantly increasing in urine after apple consumption. The multiomic dataset allowed exploration of the correlations between metabolites modulated significantly by the dietary intervention and fecal microbiota species at genus level, showing interesting interactions between Granulicatella genus and phenyl-acetic acid metabolites. Phloretin glucuronide and phloretin glucuronide sulfate appeared promising biomarkers of apple intake; however, robustness, reliability and stability data are needed for full BFI validation. CONCLUSION The identified apple BFIs can be used in future studies to assess compliance and to explore their health effects after apple intake. Moreover, the identification of polyphenol microbial metabolites suggests that apple consumption mediates significant gut microbial metabolic activity which should be further explored.
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Cullen CM, Aneja KK, Beyhan S, Cho CE, Woloszynek S, Convertino M, McCoy SJ, Zhang Y, Anderson MZ, Alvarez-Ponce D, Smirnova E, Karstens L, Dorrestein PC, Li H, Sen Gupta A, Cheung K, Powers JG, Zhao Z, Rosen GL. Emerging Priorities for Microbiome Research. Front Microbiol 2020; 11:136. [PMID: 32140140 PMCID: PMC7042322 DOI: 10.3389/fmicb.2020.00136] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 01/21/2020] [Indexed: 12/12/2022] Open
Abstract
Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.
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Affiliation(s)
- Chad M. Cullen
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
| | | | - Sinem Beyhan
- Department of Infectious Diseases, J. Craig Venter Institute, La Jolla, CA, United States
| | - Clara E. Cho
- Department of Nutrition, Dietetics and Food Sciences, Utah State University, Logan, UT, United States
| | - Stephen Woloszynek
- Ecological and Evolutionary Signal-processing and Informatics Laboratory (EESI), Electrical and Computer Engineering, Drexel University, Philadelphia, PA, United States
- College of Medicine, Drexel University, Philadelphia, PA, United States
| | - Matteo Convertino
- Nexus Group, Faculty of Information Science and Technology, Gi-CoRE Station for Big Data & Cybersecurity, Hokkaido University, Sapporo, Japan
| | - Sophie J. McCoy
- Department of Biological Science, Florida State University, Tallahassee, FL, United States
| | - Yanyan Zhang
- Department of Civil Engineering, New Mexico State University, Las Cruces, NM, United States
| | - Matthew Z. Anderson
- Department of Microbiology, The Ohio State University, Columbus, OH, United States
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, United States
| | | | - Ekaterina Smirnova
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, United States
| | - Lisa Karstens
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, United States
- Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, OR, United States
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, United States
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Ananya Sen Gupta
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, United States
| | - Kevin Cheung
- Department of Dermatology, The University of Iowa, Iowa City, IA, United States
| | | | - Zhengqiao Zhao
- Ecological and Evolutionary Signal-processing and Informatics Laboratory (EESI), Electrical and Computer Engineering, Drexel University, Philadelphia, PA, United States
| | - Gail L. Rosen
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, United States
- Ecological and Evolutionary Signal-processing and Informatics Laboratory (EESI), Electrical and Computer Engineering, Drexel University, Philadelphia, PA, United States
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Razzaq A, Sadia B, Raza A, Khalid Hameed M, Saleem F. Metabolomics: A Way Forward for Crop Improvement. Metabolites 2019; 9:E303. [PMID: 31847393 PMCID: PMC6969922 DOI: 10.3390/metabo9120303] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 12/02/2019] [Accepted: 12/11/2019] [Indexed: 12/15/2022] Open
Abstract
Metabolomics is an emerging branch of "omics" and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment-gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.
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Affiliation(s)
- Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; (A.R.); (B.S.)
| | - Bushra Sadia
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; (A.R.); (B.S.)
| | - Ali Raza
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Wuhan 430062, China;
| | - Muhammad Khalid Hameed
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China;
| | - Fozia Saleem
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; (A.R.); (B.S.)
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Nguyen DH, Nguyen CH, Mamitsuka H. Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches. Brief Bioinform 2019; 20:2028-2043. [PMID: 30099485 PMCID: PMC6954430 DOI: 10.1093/bib/bby066] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 06/14/2018] [Accepted: 07/03/2018] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.
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Affiliation(s)
- Dai Hai Nguyen
- Department of machine learning and bioinformatics, Bioinformatics Center, Kyoto University, Uji, Japan
| | - Canh Hao Nguyen
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
- Department of Computer Science, Aalto University, Otakaari, FI, Finland
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30
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Paix B, Othmani A, Debroas D, Culioli G, Briand JF. Temporal covariation of epibacterial community and surface metabolome in the Mediterranean seaweed holobiont Taonia atomaria. Environ Microbiol 2019; 21:3346-3363. [PMID: 30945796 DOI: 10.1111/1462-2920.14617] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 03/31/2019] [Indexed: 11/30/2022]
Abstract
An integrative multi-omics approach allowed monthly variations for a year of the surface metabolome and the epibacterial community of the Mediterranean Phaeophyceae Taonia atomaria to be investigated. The LC-MS-based metabolomics and 16S rDNA metabarcoding data sets were integrated in a multivariate meta-omics analysis (multi-block PLS-DA from the MixOmic DIABLO analysis) showing a strong seasonal covariation (Mantel test: p < 0.01). A network based on positive and negative correlations between the two data sets revealed two clusters of variables, one relative to the 'spring period' and a second to the 'summer period'. The 'spring period' cluster was mainly characterized by dipeptides positively correlated with a single bacterial taxon of the Alteromonadaceae family (BD1-7 clade). Moreover, 'summer' dominant epibacterial taxa from the second cluster (including Erythrobacteraceae, Rhodospirillaceae, Oceanospirillaceae and Flammeovirgaceae) showed positive correlations with few metabolites known as macroalgal antifouling defences [e.g. dimethylsulphoniopropionate (DMSP) and proline] which exhibited a key role within the correlation network. Despite a core community that represents a significant part of the total epibacteria, changes in the microbiota structure associated with surface metabolome variations suggested that both environment and algal host shape the bacterial surface microbiota.
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Affiliation(s)
- Benoît Paix
- Université de Toulon, Laboratoire MAPIEM, EA 4323, Toulon, France
| | - Ahlem Othmani
- Université de Toulon, Laboratoire MAPIEM, EA 4323, Toulon, France
| | - Didier Debroas
- Université Clermont Auvergne, CNRS, Laboratoire Microorganismes: Génome et Environnement, UMR 6023, Clermont-Ferrand, France
| | - Gérald Culioli
- Université de Toulon, Laboratoire MAPIEM, EA 4323, Toulon, France
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31
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Cantrell TP, Freeman CJ, Paul VJ, Agarwal V, Garg N. Mass Spectrometry-Based Integration and Expansion of the Chemical Diversity Harbored Within a Marine Sponge. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2019; 30:1373-1384. [PMID: 31093948 PMCID: PMC6675626 DOI: 10.1007/s13361-019-02207-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 03/27/2019] [Accepted: 03/27/2019] [Indexed: 06/09/2023]
Abstract
Marine sponges and their associated symbionts produce a structurally diverse and complex set of natural products including alkaloids, terpenoids, peptides, lipids, and steroids. A single sponge with its symbionts can produce all of the above-mentioned classes of molecules and their analogs. Most approaches to evaluating sponge chemical diversity have focused on major metabolites that can be isolated and characterized; therefore, a comprehensive evaluation of intra- (within a molecular family; analogs) and inter-chemical diversity within a single sponge remains incomplete. We use a combination of metabolomics tools, including a supervised approach via manual library search and literature search, and an unsupervised approach via molecular networking and MS2LDA analysis to describe the intra and inter-chemical diversity present in Smenospongia aurea. Furthermore, we use imaging mass spectrometry to link this chemical diversity to either the sponge or the associated cyanobacteria. Using these approaches, we identify seven more molecular features that represent analogs of four previously known peptide/polyketide smenamides and assign the biosynthesis of these molecules to the symbiotic cyanobacteria by imaging mass spectrometry. We extend this analysis to a wide diversity of molecular classes including indole alkaloids and meroterpenes.
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Affiliation(s)
- Thomas P Cantrell
- Engineered Biosystems Building, School of Chemistry and Biochemistry, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA, 30332-2000, USA
| | - Christopher J Freeman
- Smithsonian Marine Station, Smithsonian Institution, Fort Pierce, FL, 34949, USA
- Department of Biology, College of Charleston, Charleston, SC, 29424, USA
| | - Valerie J Paul
- Smithsonian Marine Station, Smithsonian Institution, Fort Pierce, FL, 34949, USA
| | - Vinayak Agarwal
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
| | - Neha Garg
- Engineered Biosystems Building, School of Chemistry and Biochemistry, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA, 30332-2000, USA.
- Center for Microbial Dynamics and Infection, School of Biological Sciences Georgia Institute of Technology, 311 Ferst Drive, ES&T Atlanta, Atlanta, GA, 30332-0230, USA.
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32
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MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools. Metabolites 2019; 9:metabo9070144. [PMID: 31315242 PMCID: PMC6680503 DOI: 10.3390/metabo9070144] [Citation(s) in RCA: 204] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 07/10/2019] [Accepted: 07/11/2019] [Indexed: 12/17/2022] Open
Abstract
Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR), and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.
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33
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Albergamo V, Schollée JE, Schymanski EL, Helmus R, Timmer H, Hollender J, de Voogt P. Nontarget Screening Reveals Time Trends of Polar Micropollutants in a Riverbank Filtration System. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:7584-7594. [PMID: 31244084 PMCID: PMC6610556 DOI: 10.1021/acs.est.9b01750] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The historic emissions of polar micropollutants in a natural drinking water source were investigated by nontarget screening with high-resolution mass spectrometry and open cheminformatics tools. The study area consisted of a riverbank filtration transect fed by the river Lek, a branch of the lower Rhine, and exhibiting up to 60-year travel time. More than 18,000 profiles were detected. Hierarchical clustering revealed that 43% of the 15 most populated clusters were characterized by intensity trends with maxima in the 1990s, reflecting intensified human activities, wastewater treatment plant upgrades and regulation in the Rhine riparian countries. Tentative structure annotation was performed using automated in silico fragmentation. Candidate structures retrieved from ChemSpider were scored based on the fit of the in silico fragments to the experimental tandem mass spectra, similarity to openly accessible accurate mass spectra, associated metadata, and presence in a suspect list. Sixty-seven unique structures (72 over both ionization modes) were tentatively identified, 25 of which were confirmed and included contaminants so far unknown to occur in bank filtrate or in natural waters at all, such as tetramethylsulfamide. This study demonstrates that many classes of hydrophilic organics enter riverbank filtration systems, persisting and migrating for decades if biogeochemical conditions are stable.
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Affiliation(s)
- Vittorio Albergamo
- Institute
for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- E-mail:
| | - Jennifer E. Schollée
- Eawag,
Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Emma L. Schymanski
- Eawag,
Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
- Luxembourg
Centre for Systems Biomedicine, University
of Luxembourg, House
of Biomedicine II 6, avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Rick Helmus
- Institute
for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Harrie Timmer
- Oasen, Nieuwe Gouwe
O.Z 3, 2801 SB Gouda, The Netherlands
| | - Juliane Hollender
- Eawag,
Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
- Institute
of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Universitätstrasse
16, 8092 Zürich, Switzerland
| | - Pim de Voogt
- Institute
for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- KWR Watercycle
Research Institute, Groningenhaven
7, 3430 BB, Nieuwegein, The Netherlands
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34
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Nothias-Esposito M, Nothias LF, Da Silva RR, Retailleau P, Zhang Z, Leyssen P, Roussi F, Touboul D, Paolini J, Dorrestein PC, Litaudon M. Investigation of Premyrsinane and Myrsinane Esters in Euphorbia cupanii and Euphobia pithyusa with MS2LDA and Combinatorial Molecular Network Annotation Propagation. JOURNAL OF NATURAL PRODUCTS 2019; 82:1459-1470. [PMID: 31181921 DOI: 10.1021/acs.jnatprod.8b00916] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The species Euphorbia pithyusa and Euphorbia cupanii are two closely related Mediterranean spurges for which their taxonomic relationships are still being debated. Herein, the diterpene ester content of E. cupanii was investigated using liquid chromatography coupled to tandem mass spectrometry. The use of molecular networking coupled to unsupervised substructure annotation ( MS2LDA) indicated the presence of new premyrsinane/myrsinane diterpene esters in the E. cupanii fractions. A structure-guided isolation procedure yielded 16 myrsinane (11a-h, 12, and 13) and premyrsinane esters (14a-c and 15a-c), along with four 4β-phorbol esters (16a-c and 17) that showed inhibitory activity against chikungunya virus replication. The structures of the 16 new compounds (11a-c, 11h, 12, 13, 14a-c, 15a-c, 16a-c, and 17) were characterized by NMR spectroscopy and X-ray crystallography. To further uncover the diterpene ester content of these two species, the concept of combinatorial network annotation propagation (C-NAP) was developed. By leveraging the fact that the diterpene esters of Euphorbia species are made up of limited building blocks, a combinatorial database of theoretical structures was created and used for C-NAP that made possible the annotation of 123 premyrsinane or myrsinane esters, from which 74% are not found in any compound database.
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Affiliation(s)
- Mélissa Nothias-Esposito
- Laboratory of Natural Products Chemistry, UMR CNRS SPE 6134 , University of Corsica , 20250 , Corte , France
- Institute of Natural Substances Chemistry, CNRS UPR 2301 , University of Paris-Saclay , 91198 , Gif-sur-Yvette , France
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Louis Felix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Ricardo R Da Silva
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Pascal Retailleau
- Institute of Natural Substances Chemistry, CNRS UPR 2301 , University of Paris-Saclay , 91198 , Gif-sur-Yvette , France
| | - Zheng Zhang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Pieter Leyssen
- Laboratory for Virology and Experimental Chemotherapy, Rega Institute for Medical Research , KU Leuven , 3000 Leuven , Belgium
| | - Fanny Roussi
- Institute of Natural Substances Chemistry, CNRS UPR 2301 , University of Paris-Saclay , 91198 , Gif-sur-Yvette , France
| | - David Touboul
- Institute of Natural Substances Chemistry, CNRS UPR 2301 , University of Paris-Saclay , 91198 , Gif-sur-Yvette , France
| | - Julien Paolini
- Laboratory of Natural Products Chemistry, UMR CNRS SPE 6134 , University of Corsica , 20250 , Corte , France
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Marc Litaudon
- Institute of Natural Substances Chemistry, CNRS UPR 2301 , University of Paris-Saclay , 91198 , Gif-sur-Yvette , France
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Walker DI, Valvi D, Rothman N, Lan Q, Miller GW, Jones DP. The metabolome: A key measure for exposome research in epidemiology. CURR EPIDEMIOL REP 2019; 6:93-103. [PMID: 31828002 PMCID: PMC6905435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
PURPOSE OF REVIEW Application of omics to study human health has created a new era of opportunities for epidemiology research. However, approaches to characterize exogenous health triggers have largely not leveraged advances in analytical platforms and big data. In this review, we highlight the exposome, which is defined as the cumulative measure of exposure and biological responses across a lifetime as a cornerstone for new epidemiology approaches to study complex and preventable human diseases. RECENT FINDINGS While no universal approach exists to measure the entirety of the exposome, use of high-resolution mass spectrometry methods provide distinct advantages over traditional biomonitoring and have provided key advances necessary for exposome research. Application to different study designs and recommendations for combining exposome data with novel data analytic frameworks to study complex interactions of multiple stressors are also discussed. SUMMARY Even though challenges still need to be addressed, advances in methods to characterize the exposome provide exciting new opportunities for epidemiology to support fundamental discoveries to improve public health.
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Affiliation(s)
- Douglas I. Walker
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Damaskini Valvi
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston MA, United States
| | - Nathaniel Rothman
- Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - Qing Lan
- Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - Gary W. Miller
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York NY
| | - Dean P. Jones
- Clinical Biomarkers Laboratory, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Atlanta, GA
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Lee LYH, Loscalzo J. Network Medicine in Pathobiology. THE AMERICAN JOURNAL OF PATHOLOGY 2019; 189:1311-1326. [PMID: 31014954 DOI: 10.1016/j.ajpath.2019.03.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 03/05/2019] [Indexed: 12/11/2022]
Abstract
The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.
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Affiliation(s)
| | - Joseph Loscalzo
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
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Colby SM, Thomas DG, Nuñez JR, Baxter DJ, Glaesemann KR, Brown JM, Pirrung MA, Govind N, Teeguarden JG, Metz TO, Renslow RS. ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries. Anal Chem 2019; 91:4346-4356. [PMID: 30741529 PMCID: PMC6526953 DOI: 10.1021/acs.analchem.8b04567] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
High-throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and environmental samples will revolutionize our understanding of the role these chemically diverse molecules play in biological systems. Despite recent technological advances, metabolomics studies still result in the detection of a disproportionate number of features that cannot be confidently assigned to a chemical structure. This inadequacy is driven by the single most significant limitation in metabolomics, the reliance on reference libraries constructed by analysis of authentic reference materials with limited commercial availability. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating libraries of chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e., conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The approach employs first-principles simulation, distinguished by the use of molecular dynamics, quantum chemistry, and ion mobility calculations, to generate structures and chemical property libraries, all without training data. Importantly, optimization of ISiCLE included a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational efficiency by over 2 orders of magnitude. Calculated CCS values were validated against 1983 experimentally measured CCS values and compared to previously reported CCS calculation approaches. Average calculated CCS error for the validation set is 3.2% using standard parameters, outperforming other density functional theory (DFT)-based methods and machine learning methods (e.g., MetCCS). An online database is introduced for sharing both calculated and experimental CCS values ( metabolomics.pnnl.gov ), initially including a CCS library with over 1 million entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including providing evidence for the presence of an environmental degradation product, the separation of molecular isomers, and an initial characterization of complex blinded mixtures of exposure chemicals. This work represents a method to address the limitations of small molecule identification and offers an alternative to generating chemical identification libraries experimentally by analyzing authentic reference materials. All code is available at github.com/pnnl .
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Affiliation(s)
- Sean M. Colby
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Dennis G. Thomas
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jamie R. Nuñez
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Douglas J. Baxter
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Kurt R. Glaesemann
- Communications and Information Technology Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Joseph M. Brown
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Meg A. Pirrung
- National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Niranjan Govind
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Justin G. Teeguarden
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon 97331, United States
| | - Thomas O. Metz
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ryan S. Renslow
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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Alygizakis NA, Gago-Ferrero P, Hollender J, Thomaidis NS. Untargeted time-pattern analysis of LC-HRMS data to detect spills and compounds with high fluctuation in influent wastewater. JOURNAL OF HAZARDOUS MATERIALS 2019; 361:19-29. [PMID: 30176412 DOI: 10.1016/j.jhazmat.2018.08.073] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 08/07/2018] [Accepted: 08/21/2018] [Indexed: 06/08/2023]
Abstract
Peak prioritization plays a key role in non-target analysis of complex samples in order to focus the elucidation efforts on potentially relevant substances. The present work shows the development of a computational workflow capable of detecting compounds that exhibit large variation in intensity over time. The developed approach is based on three open-source R packages (xcms, CAMERA and TIMECOURSE) and includes the use of the statistical test Multivariate Empirical Bayes Approach to rank the compounds based on the Hotelling T2 coefficient, which is an indicator of large concentration variations of unknown components. The approach was applied to replicate series of 24 h composite flow-proportional influent wastewater samples collected during 8 consecutive days. 60 events involving unknown substances with high fluctuation over time were successfully prioritized. 14 of those compounds were tentatively identified using HRMS/MS libraries, chemical databases, in-silico fragmentation tools, and retention time prediction models. Four compounds were confirmed with standards from which two never reported before in wastewater.
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Affiliation(s)
- Nikiforos A Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Pablo Gago-Ferrero
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Juliane Hollender
- Eawag: Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland; Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
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Carpenter CMG, Wong LYJ, Johnson CA, Helbling DE. Fall Creek Monitoring Station: Highly Resolved Temporal Sampling to Prioritize the Identification of Nontarget Micropollutants in a Small Stream. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:77-87. [PMID: 30472836 DOI: 10.1021/acs.est.8b05320] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The goal of this research was to comprehensively characterize the occurrence and temporal dynamics of target and nontarget micropollutants in a small stream. We established the Fall Creek Monitoring Station in March 2017 and collected daily composite samples for one year. We measured water samples by means of high-resolution mass spectrometry and developed and optimized a postacquisition data processing workflow to screen for 162 target micropollutants and group all mass spectral (MS) features into temporal profiles. We used hierarchical clustering analysis to prioritize nontarget MS features based their similarity to target micropollutant profiles and developed a high-throughput pipeline to elucidate the structures of prioritized nontarget MS features. Our analyses resulted in the identification of 31 target micropollutants and 59 nontarget micropollutants with varying levels of confidence. Temporal profiles of the 90 identified micropollutants revealed unexpected concentration-discharge relationships that depended on the source of the micropollutant and hydrological features of the watershed. Several of the nontarget micropollutants have not been previously reported including pharmaceutical metabolites, rubber vulcanization accelerators, plasticizers, and flame retardants. Our data provide novel insights on the temporal dynamics of micropollutant occurrence in small streams. Further, our approach to nontarget analysis is general and not restricted to highly resolved temporal data acquisitions or samples collected from surface water systems.
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Affiliation(s)
- Corey M G Carpenter
- School of Civil and Environmental Engineering , Cornell University , Ithaca , New York 14853 , United States
| | - Lok Yee J Wong
- Department of Biological and Environmental Engineering , Cornell University , Ithaca , New York 14853 , United States
| | - Catherine A Johnson
- School of Civil and Environmental Engineering , Cornell University , Ithaca , New York 14853 , United States
| | - Damian E Helbling
- School of Civil and Environmental Engineering , Cornell University , Ithaca , New York 14853 , United States
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Cui L, Lu H, Lee YH. Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases. MASS SPECTROMETRY REVIEWS 2018; 37:772-792. [PMID: 29486047 DOI: 10.1002/mas.21562] [Citation(s) in RCA: 197] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 02/02/2018] [Indexed: 05/03/2023]
Abstract
In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.
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Affiliation(s)
- Liang Cui
- Translational 'Omics and Biomarkers Group, KK Research Centre, KK Women's and Children's Hospital, Singapore, Singapore
- Infectious Diseases-Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Haitao Lu
- Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Yie Hou Lee
- Translational 'Omics and Biomarkers Group, KK Research Centre, KK Women's and Children's Hospital, Singapore, Singapore
- OBGYN-Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
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Berini JL, Brockman SA, Hegeman AD, Reich PB, Muthukrishnan R, Montgomery RA, Forester JD. Combinations of Abiotic Factors Differentially Alter Production of Plant Secondary Metabolites in Five Woody Plant Species in the Boreal-Temperate Transition Zone. FRONTIERS IN PLANT SCIENCE 2018; 9:1257. [PMID: 30233611 PMCID: PMC6134262 DOI: 10.3389/fpls.2018.01257] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/09/2018] [Indexed: 05/18/2023]
Affiliation(s)
- John L. Berini
- Conservation Biology Graduate Program, University of Minnesota, St. Paul, MN, United States
- Institute on the Environment, University of Minnesota, St. Paul, MN, United States
| | - Stephen A. Brockman
- Department of Horticultural Science, The Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, MN, United States
| | - Adrian D. Hegeman
- Department of Horticultural Science, The Microbial and Plant Genomics Institute, University of Minnesota, St. Paul, MN, United States
| | - Peter B. Reich
- Institute on the Environment, University of Minnesota, St. Paul, MN, United States
- Department of Forest Resources, University of Minnesota, St. Paul, MN, United States
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
| | - Ranjan Muthukrishnan
- Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, United States
| | - Rebecca A. Montgomery
- Institute on the Environment, University of Minnesota, St. Paul, MN, United States
- Department of Forest Resources, University of Minnesota, St. Paul, MN, United States
| | - James D. Forester
- Institute on the Environment, University of Minnesota, St. Paul, MN, United States
- Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, United States
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De Vijlder T, Valkenborg D, Lemière F, Romijn EP, Laukens K, Cuyckens F. A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation. MASS SPECTROMETRY REVIEWS 2018; 37:607-629. [PMID: 29120505 PMCID: PMC6099382 DOI: 10.1002/mas.21551] [Citation(s) in RCA: 127] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 10/03/2017] [Indexed: 05/10/2023]
Abstract
The identification of unknown molecules has been one of the cornerstone applications of mass spectrometry for decades. This tutorial reviews the basics of the interpretation of electrospray ionization-based MS and MS/MS spectra in order to identify small-molecule analytes (typically below 2000 Da). Most of what is discussed in this tutorial also applies to other atmospheric pressure ionization methods like atmospheric pressure chemical/photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds, rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M - H]- ) as well as other adduct ions, the determination of the molecular formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds (eg, contaminants in chemical production, pharmacological alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chemical derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small molecules. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.
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Affiliation(s)
- Thomas De Vijlder
- Pharmaceutical Development & Manufacturing Sciences (PDMS)Janssen Research & DevelopmentBeerseBelgium
| | - Dirk Valkenborg
- Interuniversity Institute for Biostatistics and Statistical BioinformaticsHasselt UniversityDiepenbeekBelgium
- Center for Proteomics (CFP)University of AntwerpAntwerpBelgium
- Flemish Institute for Technological Research (VITO)MolBelgium
| | - Filip Lemière
- Center for Proteomics (CFP)University of AntwerpAntwerpBelgium
- Department of Chemistry, Biomolecular and Analytical Mass SpectrometryUniversity of AntwerpAntwerpBelgium
| | - Edwin P. Romijn
- Pharmaceutical Development & Manufacturing Sciences (PDMS)Janssen Research & DevelopmentBeerseBelgium
| | - Kris Laukens
- Department of Mathematics and Computer Science, Advanced Database Research and Modelling (ADReM)University of AntwerpAntwerpBelgium
- Biomedical Informatics Network Antwerp (Biomina)University of AntwerpAntwerpBelgium
| | - Filip Cuyckens
- Pharmacokinetics, Dynamics & MetabolismJanssen Research & DevelopmentBeerseBelgium
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Dickson L, Tenon M, Svilar L, Fança-Berthon P, Lugan R, Martin JC, Vaillant F, Rogez H. Main Human Urinary Metabolites after Genipap ( Genipa americana L.) Juice Intake. Nutrients 2018; 10:E1155. [PMID: 30149503 PMCID: PMC6165415 DOI: 10.3390/nu10091155] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 08/13/2018] [Accepted: 08/17/2018] [Indexed: 12/25/2022] Open
Abstract
Genipap (Genipa americana L.) is a native fruit from Amazonia that contains bioactive compounds with a wide range of bioactivities. However, the response to genipap juice ingestion in the human exposome has never been studied. To identify biomarkers of genipap exposure, the untargeted metabolomics approach in human urine was applied. Urine samples from 16 healthy male volunteers, before and after drinking genipap juice, were analyzed by liquid chromatography⁻high-resolution mass spectrometry. XCMS package was used for data processing in the R environment and t-tests were applied on log-transformed and Pareto-scaled data to select the significant metabolites. The principal component analysis (PCA) score plots showed a clear distinction between experimental groups. Thirty-three metabolites were putatively annotated and the most discriminant were mainly related to the metabolic pathways of iridoids and phenolic derivatives. For the first time, the bioavailability of genipap iridoids after human consumption is reported. Dihydroxyhydrocinnamic acid, (1R,6R)-6-hydroxy-2-succinylcyclohexa-2,4-diene-1-carboxylate, hydroxyhydrocinnamic acid, genipic acid, 12-demethylated-8-hydroxygenipinic acid, 3(7)-dehydrogenipinic acid, genipic acid glucuronide, nonate, and 3,4-dihydroxyphenylacetate may be considered biomarkers of genipap consumption. Human exposure to genipap reveals the production of derivative forms of bioactive compounds such as genipic and genipinic acid. These findings suggest that genipap consumption triggers effects on metabolic signatures.
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Affiliation(s)
- Livia Dickson
- Federal University of Pará & Centre for Valorization of Amazonian Bioactive Compounds (CVACBA), Parque de Ciência e Tecnologia Guamá, Avenida Perimetral da Ciência, km 01, Guamá 66075-750, Brazil.
- Naturex SA, 250 rue Pierre Bayle, BP81218, 84911 Avignon CEDEX 9, France.
- Centre International de Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, TA50/PS4, 34398 Montpellier CEDEX 5, France.
| | - Mathieu Tenon
- Naturex SA, 250 rue Pierre Bayle, BP81218, 84911 Avignon CEDEX 9, France.
| | - Ljubica Svilar
- Aix Marseille Univ, INSERM, INRA, C2VN, CRIBIOM, 5-9, Boulevard Maurice Bourdet, CS 80501, 13205 Marseille CEDEX 01, France.
| | | | - Raphael Lugan
- UMR Qualisud, Université d'Avignon, 301 rue Baruch de Spinoza, BP21239, 84916 Avignon CEDEX 9, France.
| | - Jean-Charles Martin
- Aix Marseille Univ, INSERM, INRA, C2VN, CRIBIOM, 5-9, Boulevard Maurice Bourdet, CS 80501, 13205 Marseille CEDEX 01, France.
| | - Fabrice Vaillant
- Centre International de Recherche Agronomique pour le Développement (CIRAD), Avenue Agropolis, TA50/PS4, 34398 Montpellier CEDEX 5, France.
| | - Hervé Rogez
- Federal University of Pará & Centre for Valorization of Amazonian Bioactive Compounds (CVACBA), Parque de Ciência e Tecnologia Guamá, Avenida Perimetral da Ciência, km 01, Guamá 66075-750, Brazil.
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Pereira F, Aires-de-Sousa J. Computational Methodologies in the Exploration of Marine Natural Product Leads. Mar Drugs 2018; 16:md16070236. [PMID: 30011882 PMCID: PMC6070892 DOI: 10.3390/md16070236] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 07/02/2018] [Accepted: 07/06/2018] [Indexed: 12/18/2022] Open
Abstract
Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.
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Affiliation(s)
- Florbela Pereira
- LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
| | - Joao Aires-de-Sousa
- LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
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Mahmoodani F, Perera CO, Abernethy G, Fedrizzi B, Greenwood D, Chen H. Identification of Vitamin D3 Oxidation Products Using High-Resolution and Tandem Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2018; 29:1442-1455. [PMID: 29556928 DOI: 10.1007/s13361-018-1926-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 02/01/2018] [Accepted: 02/15/2018] [Indexed: 06/08/2023]
Abstract
In a successful fortification program, the stability of micronutrients added to the food is one of the most important factors. The added vitamin D3 is known to sometimes decline during storage of fortified milks, and oxidation through fatty acid lipoxidation could be suspected as the likely cause. Identification of vitamin D3 oxidation products (VDOPs) in natural foods is a challenge due to the low amount of their contents and their possible transformation to other compounds during analysis. The main objective of this study was to find a method to extract VDOPs in simulated whole milk powder and to identify these products using LTQ-ion trap, Q-Exactive Orbitrap and triple quadrupole mass spectrometry. The multistage mass spectrometry (MSn) spectra can help to propose plausible schemes for unknown compounds and their fragmentations. With the growth of combinatorial libraries, mass spectrometry (MS) has become an important analytical technique because of its speed of analysis, sensitivity, and accuracy. This study was focused on identifying the fragmentation rules for some VDOPs by incorporating MS data with in silico calculated MS fragmentation pathways. Diels-Alder derivatization was used to enhance the sensitivity and selectivity for the VDOPs' identification. Finally, the confirmed PTAD-derivatized target compounds were separated and analyzed using ESI(+)-UHPLC-MS/MS in multiple reaction monitoring (MRM) mode. Graphical Abstract ᅟ.
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Affiliation(s)
- Fatemeh Mahmoodani
- School of Chemical Sciences, Food Science Program, University of Auckland, Building 302, 23 Symonds Street, Auckland, New Zealand
| | - Conrad O Perera
- School of Chemical Sciences, Food Science Program, University of Auckland, Building 302, 23 Symonds Street, Auckland, New Zealand.
| | - Grant Abernethy
- Fonterra Cooperative Group Ltd, Palmerston North, New Zealand
| | - Bruno Fedrizzi
- School of Chemical Sciences, Food Science Program, University of Auckland, Building 302, 23 Symonds Street, Auckland, New Zealand
| | - David Greenwood
- School of Biological Sciences, University of Auckland, Building 302, 23 Symonds Street, Auckland, New Zealand
| | - Hong Chen
- Fonterra Cooperative Group Ltd, Palmerston North, New Zealand
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Ma J, Casey CP, Zheng X, Ibrahim YM, Wilkins CS, Renslow RS, Thomas DG, Payne SH, Monroe ME, Smith RD, Teeguarden JG, Baker ES, Metz TO. PIXiE: an algorithm for automated ion mobility arrival time extraction and collision cross section calculation using global data association. Bioinformatics 2018; 33:2715-2722. [PMID: 28505286 PMCID: PMC5860068 DOI: 10.1093/bioinformatics/btx305] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 05/12/2017] [Indexed: 11/24/2022] Open
Abstract
Motivation Drift tube ion mobility spectrometry coupled with mass spectrometry (DTIMS-MS) is increasingly implemented in high throughput omics workflows, and new informatics approaches are necessary for processing the associated data. To automatically extract arrival times for molecules measured by DTIMS at multiple electric fields and compute their associated collisional cross sections (CCS), we created the PNNL Ion Mobility Cross Section Extractor (PIXiE). The primary application presented for this algorithm is the extraction of data that can then be used to create a reference library of experimental CCS values for use in high throughput omics analyses. Results We demonstrate the utility of this approach by automatically extracting arrival times and calculating the associated CCSs for a set of endogenous metabolites and xenobiotics. The PIXiE-generated CCS values were within error of those calculated using commercially available instrument vendor software. Availability and implementation PIXiE is an open-source tool, freely available on Github. The documentation, source code of the software, and a GUI can be found at https://github.com/PNNL-Comp-Mass-Spec/PIXiE and the source code of the backend workflow library used by PIXiE can be found at https://github.com/PNNL-Comp-Mass-Spec/IMS-Informed-Library. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jian Ma
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Cameron P Casey
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Xueyun Zheng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Yehia M Ibrahim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Christopher S Wilkins
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Ryan S Renslow
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Dennis G Thomas
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Samuel H Payne
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Justin G Teeguarden
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.,Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 93771, USA
| | - Erin S Baker
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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48
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Blaženović I, Kind T, Ji J, Fiehn O. Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics. Metabolites 2018; 8:E31. [PMID: 29748461 PMCID: PMC6027441 DOI: 10.3390/metabo8020031] [Citation(s) in RCA: 402] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 04/26/2018] [Accepted: 05/06/2018] [Indexed: 01/17/2023] Open
Abstract
The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.
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Affiliation(s)
- Ivana Blaženović
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA.
| | - Tobias Kind
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA.
| | - Jian Ji
- State Key Laboratory of Food Science and Technology, School of Food Science of Jiangnan University, School of Food Science Synergetic Innovation Center of Food Safety and Nutrition, Wuxi 214122, China.
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA.
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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49
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Nothias LF, Nothias-Esposito M, da Silva R, Wang M, Protsyuk I, Zhang Z, Sarvepalli A, Leyssen P, Touboul D, Costa J, Paolini J, Alexandrov T, Litaudon M, Dorrestein PC. Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation. JOURNAL OF NATURAL PRODUCTS 2018; 81:758-767. [PMID: 29498278 DOI: 10.1021/acs.jnatprod.7b00737] [Citation(s) in RCA: 208] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
It is a common problem in natural product therapeutic lead discovery programs that despite good bioassay results in the initial extract, the active compound(s) may not be isolated during subsequent bioassay-guided purification. Herein, we present the concept of bioactive molecular networking to find candidate active molecules directly from fractionated bioactive extracts. By employing tandem mass spectrometry, it is possible to accelerate the dereplication of molecules using molecular networking prior to subsequent isolation of the compounds, and it is also possible to expose potentially bioactive molecules using bioactivity score prediction. Indeed, bioactivity score prediction can be calculated with the relative abundance of a molecule in fractions and the bioactivity level of each fraction. For that reason, we have developed a bioinformatic workflow able to map bioactivity score in molecular networks and applied it for discovery of antiviral compounds from a previously investigated extract of Euphorbia dendroides where the bioactive candidate molecules were not discovered following a classical bioassay-guided fractionation procedure. It can be expected that this approach will be implemented as a systematic strategy, not only in current and future bioactive lead discovery from natural extract collections but also for the reinvestigation of the untapped reservoir of bioactive analogues in previous bioassay-guided fractionation efforts.
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Affiliation(s)
- Louis-Félix Nothias
- Collaborative Mass Spectrometry Innovation Center , University of California, San Diego , La Jolla , California 92093 , United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
- Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301 , Université Paris-Sud , 91198 , Gif-sur-Yvette , France
| | - Mélissa Nothias-Esposito
- Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301 , Université Paris-Sud , 91198 , Gif-sur-Yvette , France
- Laboratoire de Chimie des Produits Naturels, CNRS, UMR SPE 6134 , University of Corsica , 20250 , Corte , France
| | - Ricardo da Silva
- Collaborative Mass Spectrometry Innovation Center , University of California, San Diego , La Jolla , California 92093 , United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center , University of California, San Diego , La Jolla , California 92093 , United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Ivan Protsyuk
- European Molecular Biology Laboratory, EMBL , Heidelberg , Germany
| | - Zheng Zhang
- Collaborative Mass Spectrometry Innovation Center , University of California, San Diego , La Jolla , California 92093 , United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Abi Sarvepalli
- Collaborative Mass Spectrometry Innovation Center , University of California, San Diego , La Jolla , California 92093 , United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Pieter Leyssen
- Laboratory for Virology and Experimental Chemotherapy, Rega Institute for Medical Research , KU Leuven , 3000 Leuven , Belgium
| | - David Touboul
- Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301 , Université Paris-Sud , 91198 , Gif-sur-Yvette , France
| | - Jean Costa
- Laboratoire de Chimie des Produits Naturels, CNRS, UMR SPE 6134 , University of Corsica , 20250 , Corte , France
| | - Julien Paolini
- Laboratoire de Chimie des Produits Naturels, CNRS, UMR SPE 6134 , University of Corsica , 20250 , Corte , France
| | - Theodore Alexandrov
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
- European Molecular Biology Laboratory, EMBL , Heidelberg , Germany
| | - Marc Litaudon
- Institut de Chimie des Substances Naturelles, CNRS, ICSN UPR 2301 , Université Paris-Sud , 91198 , Gif-sur-Yvette , France
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center , University of California, San Diego , La Jolla , California 92093 , United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
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50
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da Silva RR, Wang M, Nothias LF, van der Hooft JJJ, Caraballo-Rodríguez AM, Fox E, Balunas MJ, Klassen JL, Lopes NP, Dorrestein PC. Propagating annotations of molecular networks using in silico fragmentation. PLoS Comput Biol 2018; 14:e1006089. [PMID: 29668671 PMCID: PMC5927460 DOI: 10.1371/journal.pcbi.1006089] [Citation(s) in RCA: 189] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 04/30/2018] [Accepted: 03/13/2018] [Indexed: 12/19/2022] Open
Abstract
The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.
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Affiliation(s)
- Ricardo R. da Silva
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
- NPPNS, Department of Physic and Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Louis-Félix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Justin J. J. van der Hooft
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
- Bioinformatics Group, Department of Plant Sciences, Wageningen University, Wageningen, The Netherlands
| | - Andrés Mauricio Caraballo-Rodríguez
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Evan Fox
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, United States of America
| | - Marcy J. Balunas
- Division of Medicinal Chemistry, Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, United States of America
| | - Jonathan L. Klassen
- Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, United States of America
| | - Norberto Peporine Lopes
- NPPNS, Department of Physic and Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, United States of America
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