1
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Xu Y, Li J, Mao H, You W, Chen J, Xu H, Wu J, Gong Y, Guo L, Liu T, Li W, Xu B, Xie J. Structural annotation, semi-quantification and toxicity prediction of pyrrolizidine alkaloids from functional food: In silico and molecular networking strategy. Food Chem Toxicol 2023; 176:113738. [PMID: 37003509 DOI: 10.1016/j.fct.2023.113738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/12/2023] [Accepted: 03/19/2023] [Indexed: 04/03/2023]
Abstract
Many traditional Chinese herbs contain pyrrolizidine alkaloids (PAs), which have been reported to be toxic to livestock and humans. However, the lack of PAs standards makes it difficult to effectively conduct a risk assessment in the varied components of traditional Chinese medicine. It is necessary to propose a suitable strategy to obtain the representative occurrence data of PAs in complex systems. A comprehensive approach for annotating the structures, concentration, and mutagenicity of PAs in three Chinese herbs has been proposed in this article. First, feature-based molecular networking (FBMN) combined with network annotation propagation (NAP) on the Global Natural Products Social Molecular Networking web platform speeds up the process of annotating PAs found in Chinese herbs. Second, a semi-quantitative prediction model based on the quantitative structures and ionization intensity relationship (QSIIR) is used to forecast the amounts of PAs in complex substrates. Finally, the T.E.S.T. was used to provide predictions regarding the mutagenicity of annotated PAs. The goal of this study was to develop a strategy for combining the results of several computer models for PA screening to conduct a comprehensive analysis of PAs, which is a crucial step in risk assessment of unknown PAs in traditional Chinese herbal preparations.
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Affiliation(s)
- Yaping Xu
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Jie Li
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Huajian Mao
- Scientific Research Support Center, Academy of Military Medical Sciences, Beijing, China
| | - Wei You
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Jia Chen
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Hua Xu
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Jianfeng Wu
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Ying Gong
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Lei Guo
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China
| | - Tao Liu
- Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Wuju Li
- Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Bin Xu
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China.
| | - Jianwei Xie
- State Key Laboratory of Toxicology and Medical Countermeasures, Laboratory of Toxicant Analysis, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China.
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2
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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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3
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Shah SMZ, Ali A, Khan MN, Khadim A, Asmari M, Uddin J, Musharraf SG. Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools. Pharmaceuticals (Basel) 2022; 15:ph15070901. [PMID: 35890199 PMCID: PMC9317224 DOI: 10.3390/ph15070901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/07/2022] [Accepted: 07/11/2022] [Indexed: 12/19/2022] Open
Abstract
Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.
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Affiliation(s)
- Syed Muhammad Zaki Shah
- International Center for Chemical and Biological Sciences, H.E.J. Research Institute of Chemistry, University of Karachi, Karachi 75270, Pakistan; (S.M.Z.S.); (M.N.K.); (A.K.)
| | - Arslan Ali
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan
- Correspondence: or (A.A.); or or (S.G.M.); Tel.: +92-34819010-174 (A.A.); +92-34819010-134 (S.G.M.)
| | - Muhammad Noman Khan
- International Center for Chemical and Biological Sciences, H.E.J. Research Institute of Chemistry, University of Karachi, Karachi 75270, Pakistan; (S.M.Z.S.); (M.N.K.); (A.K.)
| | - Adeeba Khadim
- International Center for Chemical and Biological Sciences, H.E.J. Research Institute of Chemistry, University of Karachi, Karachi 75270, Pakistan; (S.M.Z.S.); (M.N.K.); (A.K.)
| | - Mufarreh Asmari
- Department of Pharmaceutical Chemistry, College of Pharmacy, Abha 62529, Saudi Arabia; (M.A.); (J.U.)
| | - Jalal Uddin
- Department of Pharmaceutical Chemistry, College of Pharmacy, Abha 62529, Saudi Arabia; (M.A.); (J.U.)
| | - Syed Ghulam Musharraf
- International Center for Chemical and Biological Sciences, H.E.J. Research Institute of Chemistry, University of Karachi, Karachi 75270, Pakistan; (S.M.Z.S.); (M.N.K.); (A.K.)
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan
- The Affiliated T.C.M Hospital of Southwest Medical University, Luzhou 646099, China
- Correspondence: or (A.A.); or or (S.G.M.); Tel.: +92-34819010-174 (A.A.); +92-34819010-134 (S.G.M.)
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4
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Rustam, Gunawan AY, Kresnowati MTAP. Data dimensionality reduction technique for clustering problem of metabolomics data. Heliyon 2022; 8:e09715. [PMID: 35721675 PMCID: PMC9201019 DOI: 10.1016/j.heliyon.2022.e09715] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 02/28/2022] [Accepted: 06/07/2022] [Indexed: 11/27/2022] Open
Abstract
In metabolomics studies, independent analyses or replicating the metabolite concentration measurements are often performed to anticipate errors. On the other hand, the size of the dataset is increasing. For clustering purposes, obtaining representative information chemically from independent analyses is needed. The objective of this study is to develop a data reduction method such that a dataset that represents chemical information is obtained. Overall a proper data reduction method would simplify the clustering of metabolite data. We propose the modified Weiszfeld algorithm (MWA) to reduce independent analyses. To obtain comprehensive results, we compare MWA with some other well-known reduction methods, including PCA, CMDS, LE, and LLE. Then reduced datasets are clustered using the fuzzy c-means (FCM) algorithm with the Tang Sun Sun (TSS) index and silhouette index as the cluster validity indices. The results show that MWA, together with PCA, present the optimal number of clusters, namely four clusters. This result aligns with the optimal number of clusters before dimensionality reduction. The present results show that MWA is robust to perform dimensionality reduction of independent analyses while maintaining chemical information on the reduced dataset. Therefore, we recommend the reliability of MWA as one of the chemometric techniques, and the present finding has enriched chemometric techniques in metabolomics studies.
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Affiliation(s)
- Rustam
- Telkom University, School of Electrical Engineering, Department of Telecommunication Engineering, Jl. Telekomunikasi No.1 Dayeuh Kolot, 40257 Kabupaten Bandung, Jawa Barat, Indonesia
| | - Agus Yodi Gunawan
- Institut Teknologi Bandung, Faculty of Mathematics and Natural Sciences, Industrial and Financial Mathematics Research Group, Jl. Ganesha 10 Bandung 40132, Indonesia
| | - Made Tri Ari Penia Kresnowati
- Institut Teknologi Bandung, Faculty of Industrial Technology, Food and Biomass Processing Technology Research Group, Jl. Ganesha 10 Bandung 40132, Indonesia
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5
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Huang D, Zhang C, Chen J, Xiao Y, Li M, Sun L, Qiu S, Chen W. Computational Workflow to Study the Diversity of Secondary Metabolites in Fourteen Different Isatis Species. Cells 2022; 11:cells11050907. [PMID: 35269530 PMCID: PMC8909408 DOI: 10.3390/cells11050907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 02/28/2022] [Accepted: 02/28/2022] [Indexed: 11/22/2022] Open
Abstract
The screening of real features among thousands of ions remains a great challenge in the study of metabolomics. In this research, a workflow designed based on the MetaboFR tool and “feature-rating” rule was developed to screen the real features in large-scale data analyses. Seventy-four reference standards were used to test the feasibility, with 83.21% of real features being obtained after MetaboFR processing. Moreover, the full workflow was applied for systematic characterization of 14 species of the genus Isatis, with the result that 87.72% of real features were retained and 69.19% of the in-source fragments were removed. To gain insights into metabolite diversity within this plant family, 1697 real features were tentatively identified, including lipids, phenylpropanoids, organic acids, indole derivatives, etc. Indole derivatives were demonstrated to be the best chemical markers with which to differentiate different species. The rare existence of indole derivatives in Isatis cappadocica (cap) and Isatis cappadocica subsp. Steveniana (capS) indicates that the biosynthesis of indole derivatives could play a key role in driving the chemical diversity and evolution of genus Isatis. Our workflow provides the foundations for the exploration of real features in metabolomics, and has the potential to reveal the chemical composition and marker metabolites of secondary metabolites in plant fields.
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Affiliation(s)
- Doudou Huang
- Research and Development Center of Chinese Medicine Resources and Biotechnology, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (D.H.); (C.Z.); (J.C.); (Y.X.); (L.S.)
| | - Chen Zhang
- Research and Development Center of Chinese Medicine Resources and Biotechnology, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (D.H.); (C.Z.); (J.C.); (Y.X.); (L.S.)
| | - Junfeng Chen
- Research and Development Center of Chinese Medicine Resources and Biotechnology, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (D.H.); (C.Z.); (J.C.); (Y.X.); (L.S.)
| | - Ying Xiao
- Research and Development Center of Chinese Medicine Resources and Biotechnology, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (D.H.); (C.Z.); (J.C.); (Y.X.); (L.S.)
| | - Mingming Li
- Department of Pharmacy, Changzheng Hospital, Second Military Medical University, Shanghai 200433, China;
| | - Lianna Sun
- Research and Development Center of Chinese Medicine Resources and Biotechnology, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (D.H.); (C.Z.); (J.C.); (Y.X.); (L.S.)
| | - Shi Qiu
- Research and Development Center of Chinese Medicine Resources and Biotechnology, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (D.H.); (C.Z.); (J.C.); (Y.X.); (L.S.)
- Correspondence: (S.Q.); (W.C.)
| | - Wansheng Chen
- Research and Development Center of Chinese Medicine Resources and Biotechnology, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (D.H.); (C.Z.); (J.C.); (Y.X.); (L.S.)
- Department of Pharmacy, Changzheng Hospital, Second Military Medical University, Shanghai 200433, China;
- Correspondence: (S.Q.); (W.C.)
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6
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Tsugawa H, Rai A, Saito K, Nakabayashi R. Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Nat Prod Rep 2021; 38:1729-1759. [PMID: 34668509 DOI: 10.1039/d1np00014d] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.
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Affiliation(s)
- Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei, Tokyo 184-8588, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Amit Rai
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Kazuki Saito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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7
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Bin Masud S, Jenkins C, Hussey E, Elkin-Frankston S, Mach P, Dhummakupt E, Aeron S. Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset. PLoS One 2021; 16:e0255240. [PMID: 34324558 PMCID: PMC8320926 DOI: 10.1371/journal.pone.0255240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 07/12/2021] [Indexed: 12/26/2022] Open
Abstract
Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers’ attention for further analysis.
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Affiliation(s)
- Shoaib Bin Masud
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA, United States of America
| | - Conor Jenkins
- DEVCOM Chemical Biological Center, Aberdeen Proving Ground, Aberdeen, MD, United States of America
| | - Erika Hussey
- DEVCOM Soldier Center, Natick, MA, United States of America
| | | | - Phillip Mach
- DEVCOM Chemical Biological Center, Aberdeen Proving Ground, Aberdeen, MD, United States of America
| | - Elizabeth Dhummakupt
- DEVCOM Chemical Biological Center, Aberdeen Proving Ground, Aberdeen, MD, United States of America
- * E-mail: (ED); (SA)
| | - Shuchin Aeron
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA, United States of America
- * E-mail: (ED); (SA)
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8
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Schweiger R, Padilla-Arizmendi F, Nogueira-López G, Rostás M, Lawry R, Brown C, Hampton J, Steyaert JM, Müller C, Mendoza-Mendoza A. Insights into Metabolic Changes Caused by the Trichoderma virens-Maize Root Interaction. MOLECULAR PLANT-MICROBE INTERACTIONS : MPMI 2021; 34:524-537. [PMID: 33166203 DOI: 10.1094/mpmi-04-20-0081-r] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The interactions of crops with root-colonizing endophytic microorganisms are highly relevant to agriculture, because endophytes can modify plant resistance to pests and increase crop yields. We investigated the interactions between the host plant Zea mays and the endophytic fungus Trichoderma virens at 5 days postinoculation grown in a hydroponic system. Wild-type T. virens and two knockout mutants, with deletion of the genes tv2og1 or vir4 involved in specialized metabolism, were analyzed. Root colonization by the fungal mutants was lower than that by the wild type. All fungal genotypes suppressed root biomass. Metabolic fingerprinting of roots, mycelia, and fungal culture supernatants was performed using ultrahigh performance liquid chromatography coupled to diode array detection and quadrupole time-of-flight tandem mass spectrometry. The metabolic composition of T. virens-colonized roots differed profoundly from that of noncolonized roots, with the effects depending on the fungal genotype. In particular, the concentrations of several metabolites derived from the shikimate pathway, including an amino acid and several flavonoids, were modulated. The expression levels of some genes coding for enzymes involved in these pathways were affected if roots were colonized by the ∆vir4 genotype of T. virens. Furthermore, mycelia and fungal culture supernatants of the different T. virens genotypes showed distinct metabolomes. Our study highlights the fact that colonization by endophytic T. virens leads to far-reaching metabolic changes, partly related to two fungal genes. Both metabolites produced by the fungus and plant metabolites modulated by the interaction probably contribute to these metabolic patterns. The metabolic changes in plant tissues may be interlinked with systemic endophyte effects often observed in later plant developmental stages.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.
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Affiliation(s)
- Rabea Schweiger
- Department of Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany
| | | | | | - Michael Rostás
- Bio-Protection Research Centre, Lincoln University, Lincoln 7647, Canterbury, New Zealand
- Agricultural Entomology, Department of Crop Sciences, University of Göttingen, Grisebachstr. 6, 37077 Göttingen, Germany
| | - Robert Lawry
- Bio-Protection Research Centre, Lincoln University, Lincoln 7647, Canterbury, New Zealand
| | - Chris Brown
- Department of Biochemistry, University of Otago, Dunedin 9054, New Zealand
| | - John Hampton
- Bio-Protection Research Centre, Lincoln University, Lincoln 7647, Canterbury, New Zealand
| | - Johanna M Steyaert
- Lincoln Agritech Ltd., PO Box 69133, Lincoln, Christchurch 7460, New Zealand
| | - Caroline Müller
- Department of Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany
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9
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Dührkop K, Nothias LF, Fleischauer M, Reher R, Ludwig M, Hoffmann MA, Petras D, Gerwick WH, Rousu J, Dorrestein PC, Böcker S. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol 2021; 39:462-471. [PMID: 33230292 DOI: 10.1038/s41587-020-0740-8] [Citation(s) in RCA: 252] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 10/16/2020] [Indexed: 12/12/2022]
Abstract
Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.
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Affiliation(s)
- Kai Dührkop
- Chair for Bioinformatics, Friedrich-Schiller University, Jena, Germany
| | - Louis-Félix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Raphael Reher
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
| | - Marcus Ludwig
- Chair for Bioinformatics, Friedrich-Schiller University, Jena, Germany
| | - Martin A Hoffmann
- Chair for Bioinformatics, Friedrich-Schiller University, Jena, Germany
- International Max Planck Research School 'Exploration of Ecological Interactions with Molecular and Chemical Techniques', Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Daniel Petras
- Collaborative Mass Spectrometry Innovation Center, 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
| | - William H Gerwick
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Juho Rousu
- Helsinki Institute for Information Technology, Department of Computer Science, Aalto University, Espoo, Finland
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Sebastian Böcker
- Chair for Bioinformatics, Friedrich-Schiller University, Jena, Germany.
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10
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Peters K, Balcke G, Kleinenkuhnen N, Treutler H, Neumann S. Untargeted In Silico Compound Classification-A Novel Metabolomics Method to Assess the Chemodiversity in Bryophytes. Int J Mol Sci 2021; 22:ijms22063251. [PMID: 33806786 PMCID: PMC8005083 DOI: 10.3390/ijms22063251] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 12/29/2022] Open
Abstract
In plant ecology, biochemical analyses of bryophytes and vascular plants are often conducted on dried herbarium specimen as species typically grow in distant and inaccessible locations. Here, we present an automated in silico compound classification framework to annotate metabolites using an untargeted data independent acquisition (DIA)–LC/MS–QToF-sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) ecometabolomics analytical method. We perform a comparative investigation of the chemical diversity at the global level and the composition of metabolite families in ten different species of bryophytes using fresh samples collected on-site and dried specimen stored in a herbarium for half a year. Shannon and Pielou’s diversity indices, hierarchical clustering analysis (HCA), sparse partial least squares discriminant analysis (sPLS-DA), distance-based redundancy analysis (dbRDA), ANOVA with post-hoc Tukey honestly significant difference (HSD) test, and the Fisher’s exact test were used to determine differences in the richness and composition of metabolite families, with regard to herbarium conditions, ecological characteristics, and species. We functionally annotated metabolite families to biochemical processes related to the structural integrity of membranes and cell walls (proto-lignin, glycerophospholipids, carbohydrates), chemical defense (polyphenols, steroids), reactive oxygen species (ROS) protection (alkaloids, amino acids, flavonoids), nutrition (nitrogen- and phosphate-containing glycerophospholipids), and photosynthesis. Changes in the composition of metabolite families also explained variance related to ecological functioning like physiological adaptations of bryophytes to dry environments (proteins, peptides, flavonoids, terpenes), light availability (flavonoids, terpenes, carbohydrates), temperature (flavonoids), and biotic interactions (steroids, terpenes). The results from this study allow to construct chemical traits that can be attributed to biogeochemistry, habitat conditions, environmental changes and biotic interactions. Our classification framework accelerates the complex annotation process in metabolomics and can be used to simplify biochemical patterns. We show that compound classification is a powerful tool that allows to explore relationships in both molecular biology by “zooming in” and in ecology by “zooming out”. The insights revealed by our framework allow to construct new research hypotheses and to enable detailed follow-up studies.
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Affiliation(s)
- Kristian Peters
- Bioinformatics & Scientific Data, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle (Saale), Germany; (H.T.); (S.N.)
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
- Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, 06108 Halle (Saale), Germany
- Correspondence: ; Tel.: +49-345-5582-1475
| | - Gerd Balcke
- Cell and Metabolic Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle (Saale), Germany;
| | - Niklas Kleinenkuhnen
- Max Planck Research Group Chromatin and Ageing, Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Str. 9b, 50931 Cologne, Germany;
- MS-Platform, Cluster of Excellence on Plant Sciences, Botanical Institute (CEPLAS), University of Cologne, 50931 Cologne, Germany
| | - Hendrik Treutler
- Bioinformatics & Scientific Data, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle (Saale), Germany; (H.T.); (S.N.)
- Datameer GmbH, Magdeburger Straße 23, 06112 Halle (Saale), Germany
| | - Steffen Neumann
- Bioinformatics & Scientific Data, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle (Saale), Germany; (H.T.); (S.N.)
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
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11
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Çolak NG, Eken NT, Frary A, Doğanlar S. Chromatographic Analysis for Targeted Metabolomics of Antioxidant and Flavor-Related Metabolites in Tomato. Bio Protoc 2021; 11:e3929. [PMID: 33796605 DOI: 10.21769/bioprotoc.3929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/20/2020] [Accepted: 12/20/2020] [Indexed: 11/02/2022] Open
Abstract
Targeted metabolomics is a useful approach to evaluate crop breeding studies. Antioxidant and flavor-related traits are of increasing interest and are considered quality traits in tomato breeding. The present study presents chromatographic methods to study antioxidants (carotenoids, vitamin C, vitamin E, phenolic compounds, and glutathione) and flavor-related characters (sugars and organic acids) in tomato. Two different extraction methods (for polar and apolar entities) were applied to isolate the targeted compounds. The extraction methods developed in this work were time and cost-effective since no further purification was needed. Carotenoids, vitamin C, glutathione, and phenolic acids were analyzed by HPLC-PDA using a RP C18 column at an appropriate wavelength for each compound. Vitamin E and sugars were analyzed by HPLC with RP C18 and NH2 columns and detected by FLD and RI detectors, respectively. In addition, organic acids were analyzed with GC-FID using a Rtx 5DA column after derivatization with MSTFA. As a result, sensitive analytical methods to quantify important plant metabolites were developed and are described herein. These methods are not only applicable in tomato but are also useful to characterize other species for flavor-related and antioxidant compounds. Thus, these protocols can be used to guide selection in crop breeding.
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Affiliation(s)
- Nergiz Gürbüz Çolak
- Department of Molecular Biology and Genetics, Faculty of Science, Izmir Institute of Technology, Izmir, Turkey.,Plant Science and Technology and Application Center, Izmir Institute of Technology, Izmir, Turkey
| | - Neslihan Tek Eken
- Department of Molecular Biology and Genetics, Faculty of Science, Izmir Institute of Technology, Izmir, Turkey
| | - Anne Frary
- Department of Molecular Biology and Genetics, Faculty of Science, Izmir Institute of Technology, Izmir, Turkey
| | - Sami Doğanlar
- Department of Molecular Biology and Genetics, Faculty of Science, Izmir Institute of Technology, Izmir, Turkey.,Plant Science and Technology and Application Center, Izmir Institute of Technology, Izmir, Turkey
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12
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Chemically informed analyses of metabolomics mass spectrometry data with Qemistree. Nat Chem Biol 2021; 17:146-151. [PMID: 33199911 PMCID: PMC8189545 DOI: 10.1038/s41589-020-00677-3] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 09/18/2020] [Indexed: 01/28/2023]
Abstract
Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.
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13
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Orešič M, McGlinchey A, Wheelock CE, Hyötyläinen T. Metabolic Signatures of the Exposome-Quantifying the Impact of Exposure to Environmental Chemicals on Human Health. Metabolites 2020; 10:metabo10110454. [PMID: 33182712 PMCID: PMC7698239 DOI: 10.3390/metabo10110454] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/08/2020] [Accepted: 11/09/2020] [Indexed: 02/06/2023] Open
Abstract
Human health and well-being are intricately linked to environmental quality. Environmental exposures can have lifelong consequences. In particular, exposures during the vulnerable fetal or early development period can affect structure, physiology and metabolism, causing potential adverse, often permanent, health effects at any point in life. External exposures, such as the “chemical exposome” (exposures to environmental chemicals), affect the host’s metabolism and immune system, which, in turn, mediate the risk of various diseases. Linking such exposures to adverse outcomes, via intermediate phenotypes such as the metabolome, is one of the central themes of exposome research. Much progress has been made in this line of research, including addressing some key challenges such as analytical coverage of the exposome and metabolome, as well as the integration of heterogeneous, multi-omics data. There is strong evidence that chemical exposures have a marked impact on the metabolome, associating with specific disease risks. Herein, we review recent progress in the field of exposome research as related to human health as well as selected metabolic and autoimmune diseases, with specific emphasis on the impacts of chemical exposures on the host metabolome.
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Affiliation(s)
- Matej Orešič
- School of Medical Sciences, Örebro University, SE-701 82 Örebro, Sweden; (M.O.); (A.M.)
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
| | - Aidan McGlinchey
- School of Medical Sciences, Örebro University, SE-701 82 Örebro, Sweden; (M.O.); (A.M.)
| | - Craig E. Wheelock
- Division of Physiological Chemistry II, Department of Medical Biochemistry and Biophysics, Karolinska Institute, SE-171 77 Stockholm, Sweden;
| | - Tuulia Hyötyläinen
- MTM Research Centre, School of Science and Technology, Örebro University, SE-701 82 Örebro, Sweden
- Correspondence:
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14
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Wright Muelas M, Roberts I, Mughal F, O'Hagan S, Day PJ, Kell DB. An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines. Metabolomics 2020; 16:107. [PMID: 33026554 PMCID: PMC7541387 DOI: 10.1007/s11306-020-01725-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 09/18/2020] [Indexed: 02/07/2023]
Abstract
INTRODUCTION It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites. OBJECTIVES Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines. METHODS We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the 'exometabolome' or metabolic footprint). RESULTS Our method measures some 4000-5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other. CONCLUSION Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.
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Affiliation(s)
- Marina Wright Muelas
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
| | - Ivayla Roberts
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Farah Mughal
- School of Chemistry, The Manchester Institute of Biotechnology, 131, Princess St, Manchester, M1 7DN, UK
- The Manchester Institute of Biotechnology, 131, Princess St, Manchester, M1 7DN, UK
| | - Steve O'Hagan
- The Manchester Institute of Biotechnology, 131, Princess St, Manchester, M1 7DN, UK
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK
| | - Philip J Day
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Chemitorvet, Kgs Lyngby, 2000, Denmark
| | - Douglas B Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
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15
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Dietz S, Herz K, Gorzolka K, Jandt U, Bruelheide H, Scheel D. Root exudate composition of grass and forb species in natural grasslands. Sci Rep 2020; 10:10691. [PMID: 32612150 PMCID: PMC7329890 DOI: 10.1038/s41598-019-54309-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 09/25/2019] [Indexed: 11/08/2022] Open
Abstract
Plants exude a diverse cocktail of metabolites into the soil as response to exogenous and endogenous factors. So far, root exudates have mainly been studied under artificial conditions due to methodological difficulties. In this study, each five perennial grass and forb species were investigated for polar and semi-polar metabolites in exudates under field conditions. Metabolite collection and untargeted profiling approaches combined with a novel classification method allowed the designation of 182 metabolites. The composition of exuded polar metabolites depended mainly on the local environment, especially soil conditions, whereas the pattern of semi-polar metabolites was primarily affected by the species identity. The profiles of both polar and semi-polar metabolites differed between growth forms, with grass species being generally more similar to each other and more responsive to the abiotic environment than forb species. This study demonstrated the feasibility of investigating exudates under field conditions and to identify the driving factors of exudate composition.
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Affiliation(s)
- Sophie Dietz
- Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle (Saale), Germany.
| | - Katharina Herz
- Martin Luther University Halle-Wittenberg, Institute of Biology/Geobotany and Botanical Garden, Am Kirchtor 1, 06108, Halle [Saale], Germany
| | - Karin Gorzolka
- Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle (Saale), Germany
| | - Ute Jandt
- Martin Luther University Halle-Wittenberg, Institute of Biology/Geobotany and Botanical Garden, Am Kirchtor 1, 06108, Halle [Saale], Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany
| | - Helge Bruelheide
- Martin Luther University Halle-Wittenberg, Institute of Biology/Geobotany and Botanical Garden, Am Kirchtor 1, 06108, Halle [Saale], Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany
| | - Dierk Scheel
- Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany
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16
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Depke T, Franke R, Brönstrup M. CluMSID: an R package for similarity-based clustering of tandem mass spectra to aid feature annotation in metabolomics. Bioinformatics 2020; 35:3196-3198. [PMID: 30649189 DOI: 10.1093/bioinformatics/btz005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 11/12/2018] [Accepted: 01/07/2019] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Compound identification is one of the most eminent challenges in the untargeted analysis of complex mixtures of small molecules by mass spectrometry. Similarity of tandem mass spectra can provide valuable information on putative structural similarities between known and unknown analytes and hence aids feature identification in the bioanalytical sciences. We have developed CluMSID (Clustering of MS2 spectra for metabolite identification), an R package that enables researchers to make use of tandem mass spectra and neutral loss pattern similarities as a part of their metabolite annotation workflow. CluMSID offers functions for all analysis steps from import of raw data to data mining by unsupervised multivariate methods along with respective (interactive) visualizations. A detailed tutorial with example data is provided as supplementary information. AVAILABILITY AND IMPLEMENTATION CluMSID is available as R package from https://github.com/tdepke/CluMSID/and from https://bioconductor.org/packages/CluMSID/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tobias Depke
- Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig D-38124, Germany
| | - Raimo Franke
- Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig D-38124, Germany
| | - Mark Brönstrup
- Department of Chemical Biology, Helmholtz Centre for Infection Research, Braunschweig D-38124, Germany.,German Centre for Infection Research (DZIF), partner site Hannover-Braunschweig, D-38124 Braunschweig, Germany
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17
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Leonova T, Popova V, Tsarev A, Henning C, Antonova K, Rogovskaya N, Vikhnina M, Baldensperger T, Soboleva A, Dinastia E, Dorn M, Shiroglasova O, Grishina T, Balcke GU, Ihling C, Smolikova G, Medvedev S, Zhukov VA, Babakov V, Tikhonovich IA, Glomb MA, Bilova T, Frolov A. Does Protein Glycation Impact on the Drought-Related Changes in Metabolism and Nutritional Properties of Mature Pea ( Pisum sativum L.) Seeds? Int J Mol Sci 2020; 21:E567. [PMID: 31952342 PMCID: PMC7013545 DOI: 10.3390/ijms21020567] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 01/01/2020] [Accepted: 01/03/2020] [Indexed: 12/24/2022] Open
Abstract
Protein glycation is usually referred to as an array of non-enzymatic post-translational modifications formed by reducing sugars and carbonyl products of their degradation. The resulting advanced glycation end products (AGEs) represent a heterogeneous group of covalent adducts, known for their pro-inflammatory effects in mammals, and impacting on pathogenesis of metabolic diseases and ageing. In plants, AGEs are the markers of tissue ageing and response to environmental stressors, the most prominent of which is drought. Although water deficit enhances protein glycation in leaves, its effect on seed glycation profiles is still unknown. Moreover, the effect of drought on biological activities of seed protein in mammalian systems is still unstudied with respect to glycation. Therefore, here we address the effects of a short-term drought on the patterns of seed protein-bound AGEs and accompanying alterations in pro-inflammatory properties of seed protein in the context of seed metabolome dynamics. A short-term drought, simulated as polyethylene glycol-induced osmotic stress and applied at the stage of seed filling, resulted in the dramatic suppression of primary seed metabolism, although the secondary metabolome was minimally affected. This was accompanied with significant suppression of NF-kB activation in human SH-SY5Y neuroblastoma cells after a treatment with protein hydrolyzates, isolated from the mature seeds of drought-treated plants. This effect could not be attributed to formation of known AGEs. Most likely, the prospective anti-inflammatory effect of short-term drought is related to antioxidant effect of unknown secondary metabolite protein adducts, or down-regulation of unknown plant-specific AGEs due to suppression of energy metabolism during seed filling.
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Affiliation(s)
- Tatiana Leonova
- Department of Biochemistry, St. Petersburg State University, 199004 St. Petersburg, Russia
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Veronika Popova
- Department of Biochemistry, St. Petersburg State University, 199004 St. Petersburg, Russia
- Department of Plant Physiology and Biochemistry, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Alexander Tsarev
- Department of Biochemistry, St. Petersburg State University, 199004 St. Petersburg, Russia
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Christian Henning
- Institute of Chemistry - Food Chemistry, Martin-Luther University Halle-Wittenberg, 06120 Halle, Germany
| | - Kristina Antonova
- Department of Biochemistry, St. Petersburg State University, 199004 St. Petersburg, Russia
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Nadezhda Rogovskaya
- Research Institute of Hygiene, Occupational Pathology and Human Ecology, 188663 Leningrad Oblast, Russia
| | - Maria Vikhnina
- Department of Biochemistry, St. Petersburg State University, 199004 St. Petersburg, Russia
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Tim Baldensperger
- Institute of Chemistry - Food Chemistry, Martin-Luther University Halle-Wittenberg, 06120 Halle, Germany
| | - Alena Soboleva
- Department of Biochemistry, St. Petersburg State University, 199004 St. Petersburg, Russia
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Ekaterina Dinastia
- Department of Biochemistry, St. Petersburg State University, 199004 St. Petersburg, Russia
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
- Postovsky Institute of Organic Synthesis of Ural Division of Russian Academy of Sciences, 620137 Yekaterinburg, Russia
| | - Mandy Dorn
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Olga Shiroglasova
- Department of Plant Physiology and Biochemistry, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Tatiana Grishina
- Department of Biochemistry, St. Petersburg State University, 199004 St. Petersburg, Russia
| | - Gerd U Balcke
- Department of Metabolic and Cell Biology, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
| | - Christian Ihling
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin-Luther University Halle-Wittenberg, 06120 Halle, Germany
| | - Galina Smolikova
- Department of Plant Physiology and Biochemistry, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Sergei Medvedev
- Department of Plant Physiology and Biochemistry, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Vladimir A Zhukov
- All-Russia Research Institute for Agricultural Microbiology, 196608 St. Petersburg, Russia
| | - Vladimir Babakov
- Research Institute of Hygiene, Occupational Pathology and Human Ecology, 188663 Leningrad Oblast, Russia
| | - Igor A Tikhonovich
- All-Russia Research Institute for Agricultural Microbiology, 196608 St. Petersburg, Russia
- Department of Genetics and Biotechnology, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Marcus A Glomb
- Institute of Chemistry - Food Chemistry, Martin-Luther University Halle-Wittenberg, 06120 Halle, Germany
| | - Tatiana Bilova
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
- Department of Plant Physiology and Biochemistry, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - Andrej Frolov
- Department of Biochemistry, St. Petersburg State University, 199004 St. Petersburg, Russia
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, 06120 Halle, Germany
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18
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Nelson BS, Lin L, Kremer DM, Sousa CM, Cotta-Ramusino C, Myers A, Ramos J, Gao T, Kovalenko I, Wilder-Romans K, Dresser J, Davis M, Lee HJ, Nwosu ZC, Campit S, Mashadova O, Nicolay BN, Tolstyka ZP, Halbrook CJ, Chandrasekaran S, Asara JM, Crawford HC, Cantley LC, Kimmelman AC, Wahl DR, Lyssiotis CA. Tissue of origin dictates GOT1 dependence and confers synthetic lethality to radiotherapy. Cancer Metab 2020; 8:1. [PMID: 31908776 PMCID: PMC6941320 DOI: 10.1186/s40170-019-0202-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 11/20/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Metabolic programs in cancer cells are influenced by genotype and the tissue of origin. We have previously shown that central carbon metabolism is rewired in pancreatic ductal adenocarcinoma (PDA) to support proliferation through a glutamate oxaloacetate transaminase 1 (GOT1)-dependent pathway. METHODS We utilized a doxycycline-inducible shRNA-mediated strategy to knockdown GOT1 in PDA and colorectal cancer (CRC) cell lines and tumor models of similar genotype. These cells were analyzed for the ability to form colonies and tumors to test if tissue type impacted GOT1 dependence. Additionally, the ability of GOT1 to impact the response to chemo- and radiotherapy was assessed. Mechanistically, the associated specimens were examined using a combination of steady-state and stable isotope tracing metabolomics strategies and computational modeling. Statistics were calculated using GraphPad Prism 7. One-way ANOVA was performed for experiments comparing multiple groups with one changing variable. Student's t test (unpaired, two-tailed) was performed when comparing two groups to each other. Metabolomics data comparing three PDA and three CRC cell lines were analyzed by performing Student's t test (unpaired, two-tailed) between all PDA metabolites and CRC metabolites. RESULTS While PDA exhibits profound growth inhibition upon GOT1 knockdown, we found CRC to be insensitive. In PDA, but not CRC, GOT1 inhibition disrupted glycolysis, nucleotide metabolism, and redox homeostasis. These insights were leveraged in PDA, where we demonstrate that radiotherapy potently enhanced the effect of GOT1 inhibition on tumor growth. CONCLUSIONS Taken together, these results illustrate the role of tissue type in dictating metabolic dependencies and provide new insights for targeting metabolism to treat PDA.
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Affiliation(s)
- Barbara S. Nelson
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215 USA
- Experimental Therapeutics Core and Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215 USA
- Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Meyer Cancer Center, Weill Cornell Medicine, New York City, NY 10065 USA
- Agios Pharmaceuticals, Inc., Cambridge, MA 02139 USA
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115 USA
- Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Radiation Oncology, Perlmutter Cancer Center, NYU Langone Medical Center, New York, NY 10016 USA
| | - Lin Lin
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215 USA
- Experimental Therapeutics Core and Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215 USA
- Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Meyer Cancer Center, Weill Cornell Medicine, New York City, NY 10065 USA
- Agios Pharmaceuticals, Inc., Cambridge, MA 02139 USA
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115 USA
- Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Radiation Oncology, Perlmutter Cancer Center, NYU Langone Medical Center, New York, NY 10016 USA
| | - Daniel M. Kremer
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Cristovão M. Sousa
- Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215 USA
- Agios Pharmaceuticals, Inc., Cambridge, MA 02139 USA
| | - Cecilia Cotta-Ramusino
- Experimental Therapeutics Core and Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215 USA
| | - Amy Myers
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Johanna Ramos
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Tina Gao
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Ilya Kovalenko
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Kari Wilder-Romans
- Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Joseph Dresser
- Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Mary Davis
- Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Ho-Joon Lee
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Zeribe C. Nwosu
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Scott Campit
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Oksana Mashadova
- Meyer Cancer Center, Weill Cornell Medicine, New York City, NY 10065 USA
| | | | - Zachary P. Tolstyka
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Christopher J. Halbrook
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - John M. Asara
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115 USA
| | - Howard C. Crawford
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Lewis C. Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York City, NY 10065 USA
| | - Alec C. Kimmelman
- Division of Genomic Stability and DNA Repair, Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215 USA
- Department of Radiation Oncology, Perlmutter Cancer Center, NYU Langone Medical Center, New York, NY 10016 USA
| | - Daniel R. Wahl
- Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Costas A. Lyssiotis
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
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19
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Cai Y, Rosen Vollmar AK, Johnson CH. Analyzing Metabolomics Data for Environmental Health and Exposome Research. Methods Mol Biol 2020; 2104:447-467. [PMID: 31953830 DOI: 10.1007/978-1-0716-0239-3_22] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The exposome is the cumulative measure of environmental influences and associated biological responses across the life span, with critical relevance for understanding how exposures can impact human health. Metabolomics analysis of biological samples offers unique advantages for examining the exposome. Simultaneous analysis of external exposures, biological responses, and host susceptibility at a systems level can help establish links between external exposures and health outcomes. As metabolomics technologies continue to evolve for the study of the exposome, metabolomics ultimately will help provide valuable insights for exposure risk assessment, and disease prevention and management. Here, we discuss recent advances in metabolomics, and describe data processing protocols that can enable analysis of the exposome. This chapter focuses on using liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics for analysis of the exposome, including (1) preprocessing of untargeted metabolomics data, (2) identification of exposure chemicals and their metabolites, and (3) methods to establish associations between exposures and diseases.
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Affiliation(s)
- Yuping Cai
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Ana K Rosen Vollmar
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Caroline Helen Johnson
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.
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20
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Naake T, Gaquerel E, Fernie AR. Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics. Methods Mol Biol 2020; 2104:209-225. [PMID: 31953820 DOI: 10.1007/978-1-0716-0239-3_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.
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Affiliation(s)
- Thomas Naake
- Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Emmanuel Gaquerel
- Institute of Plant Molecular Biology, University of Strasbourg, Strasbourg, France.,Centre for Organismal Studies, University of Heidelberg, Heidelberg, Germany
| | - Alisdair R Fernie
- Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
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21
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From mass to metabolite in human untargeted metabolomics: Recent advances in annotation of metabolites applying liquid chromatography-mass spectrometry data. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2018.11.022] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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22
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Doppler M, Bueschl C, Kluger B, Koutnik A, Lemmens M, Buerstmayr H, Rechthaler J, Krska R, Adam G, Schuhmacher R. Stable Isotope-Assisted Plant Metabolomics: Combination of Global and Tracer-Based Labeling for Enhanced Untargeted Profiling and Compound Annotation. FRONTIERS IN PLANT SCIENCE 2019; 10:1366. [PMID: 31708958 PMCID: PMC6824187 DOI: 10.3389/fpls.2019.01366] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 10/04/2019] [Indexed: 05/10/2023]
Abstract
Untargeted approaches and thus biological interpretation of metabolomics results are still hampered by the reliable assignment of the global metabolome as well as classification and (putative) identification of metabolites. In this work we present an liquid chromatography-mass spectrometry (LC-MS)-based stable isotope assisted approach that combines global metabolome and tracer based isotope labeling for improved characterization of (unknown) metabolites and their classification into tracer derived submetabolomes. To this end, wheat plants were cultivated in a customized growth chamber, which was kept at 400 ± 50 ppm 13CO2 to produce highly enriched uniformly 13C-labeled sample material. Additionally, native plants were grown in the greenhouse and treated with either 13C9-labeled phenylalanine (Phe) or 13C11-labeled tryptophan (Trp) to study their metabolism and biochemical pathways. After sample preparation, liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis and automated data evaluation, the results of the global metabolome- and tracer-labeling approaches were combined. A total of 1,729 plant metabolites were detected out of which 122 respective 58 metabolites account for the Phe- and Trp-derived submetabolomes. Besides m/z and retention time, also the total number of carbon atoms as well as those of the incorporated tracer moieties were obtained for the detected metabolite ions. With this information at hand characterization of unknown compounds was improved as the additional knowledge from the tracer approaches considerably reduced the number of plausible sum formulas and structures of the detected metabolites. Finally, the number of putative structure formulas was further reduced by isotope-assisted annotation tandem mass spectrometry (MS/MS) derived product ion spectra of the detected metabolites. A major innovation of this paper is the classification of the metabolites into submetabolomes which turned out to be valuable information for effective filtering of database hits based on characteristic structural subparts. This allows the generation of a final list of true plant metabolites, which can be characterized at different levels of specificity.
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Affiliation(s)
- Maria Doppler
- Department of Agrobiotechnology (IFA-Tulln), Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
| | - Christoph Bueschl
- Department of Agrobiotechnology (IFA-Tulln), Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
| | - Bernhard Kluger
- Department of Agrobiotechnology (IFA-Tulln), Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
| | - Andrea Koutnik
- Department of Agrobiotechnology (IFA-Tulln), Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
| | - Marc Lemmens
- Department of Agrobiotechnology (IFA-Tulln), Institute for Biotechnology in Plant Production, University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
| | - Hermann Buerstmayr
- Department of Agrobiotechnology (IFA-Tulln), Institute for Biotechnology in Plant Production, University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
| | - Justyna Rechthaler
- University of Applied Sciences Wr. Neustadt, Degree Programme Biotechnical Processes (FHWN-Tulln), Tulln, Austria
| | - Rudolf Krska
- Department of Agrobiotechnology (IFA-Tulln), Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, United Kingdom
| | - Gerhard Adam
- Department of Applied Genetics and Cell Biology (DAGZ), University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
| | - Rainer Schuhmacher
- Department of Agrobiotechnology (IFA-Tulln), Institute of Bioanalytics and Agro-Metabolomics, University of Natural Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
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23
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Chemical Diversity and Classification of Secondary Metabolites in Nine Bryophyte Species. Metabolites 2019; 9:metabo9100222. [PMID: 31614655 PMCID: PMC6835487 DOI: 10.3390/metabo9100222] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 10/01/2019] [Accepted: 10/08/2019] [Indexed: 11/28/2022] Open
Abstract
The central aim in ecometabolomics and chemical ecology is to pinpoint chemical features that explain molecular functioning. The greatest challenge is the identification of compounds due to the lack of constitutive reference spectra, the large number of completely unknown compounds, and bioinformatic methods to analyze the big data. In this study we present an interdisciplinary methodological framework that extends ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) and the automated in silico classification of fragment peaks into compound classes. We synthesize findings from a prior study that explored the influence of seasonal variations on the chemodiversity of secondary metabolites in nine bryophyte species. Here we reuse and extend the representative dataset with DDA-MS data. Hierarchical clustering, heatmaps, dbRDA, and ANOVA with post-hoc Tukey HSD were used to determine relationships of the study factors species, seasons, and ecological characteristics. The tested bryophytes showed species-specific metabolic responses to seasonal variations (50% vs. 5% of explained variation). Marchantia polymorpha, Plagiomnium undulatum, and Polytrichum strictum were biochemically most diverse and unique. Flavonoids and sesquiterpenoids were upregulated in all bryophytes in the growing seasons. We identified ecological functioning of compound classes indicating light protection (flavonoids), biotic and pathogen interactions (sesquiterpenoids, flavonoids), low temperature and desiccation tolerance (glycosides, sesquiterpenoids, anthocyanins, lactones), and moss growth supporting anatomic structures (few methoxyphenols and cinnamic acids as part of proto-lignin constituents). The reusable bioinformatic framework of this study can differentiate species based on automated compound classification. Our study allows detailed insights into the ecological roles of biochemical constituents of bryophytes with regard to seasonal variations. We demonstrate that compound classification can be improved with adding constitutive reference spectra to existing spectral libraries. We also show that generalization on compound classes improves our understanding of molecular ecological functioning and can be used to generate new research hypotheses.
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24
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Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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25
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Peters DL, Wang W, Zhang X, Ning Z, Mayne J, Figeys D. Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome. Proteomics 2019; 19:e1800363. [PMID: 31321880 DOI: 10.1002/pmic.201800363] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 06/27/2019] [Indexed: 12/14/2022]
Abstract
The gut microbiome has been shown to play a significant role in human healthy and diseased states. The dynamic signaling that occurs between the host and microbiome is critical for the maintenance of host homeostasis. Analyzing the human microbiome with metaproteomics, metabolomics, and integrative multi-omics analyses can provide significant information on markers for healthy and diseased states, allowing for the eventual creation of microbiome-targeted treatments for diseases associated with dysbiosis. Metaproteomics enables functional activity information to be gained from the microbiome samples, while metabolomics provides insight into the overall metabolic states affecting/representing the host-microbiome interactions. Combining these functional -omic platforms together with microbiome composition profiling allows for a holistic overview on the functional and metabolic state of the microbiome and its influence on human health. Here the benefits of metaproteomics, metabolomics, and the integrative multi-omic approaches to investigating the gut microbiome in the context of human health and diseases are reviewed.
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Affiliation(s)
- Danielle L Peters
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Wenju Wang
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Xu Zhang
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Zhibin Ning
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Janice Mayne
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Daniel Figeys
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada.,Canadian Institute for Advanced Research, 661 University Ave, Toronto, ON, M5G 1M1, Canada.,The University of Ottawa and Shanghai Institute of Materia Medica Joint Research Center on Systems and Personalized Pharmacology, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
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26
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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: 199] [Impact Index Per Article: 39.8] [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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27
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Effects of Water Availability in the Soil on Tropane Alkaloid Production in Cultivated Datura stramonium. Metabolites 2019; 9:metabo9070131. [PMID: 31277288 PMCID: PMC6680536 DOI: 10.3390/metabo9070131] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 06/26/2019] [Accepted: 06/28/2019] [Indexed: 12/14/2022] Open
Abstract
Background: different Solanaceae and Erythroxylaceae species produce tropane alkaloids. These alkaloids are the starting material in the production of different pharmaceuticals. The commercial demand for tropane alkaloids is covered by extracting them from cultivated plants. Datura stramonium is cultivated under greenhouse conditions as a source of tropane alkaloids. Here we investigate the effect of different levels of water availability in the soil on the production of tropane alkaloids by D. stramonium. Methods: We tested four irrigation levels on the accumulation of tropane alkaloids. We analyzed the profile of tropane alkaloids using an untargeted liquid chromatography/mass spectrometry method. Results: Using a combination of informatics and manual interpretation of mass spectra, we generated several structure hypotheses for signals in D. stramonium extracts that we assign as putative tropane alkaloids. Quantitation of mass spectrometry signals for our structure hypotheses across different anatomical organs allowed us to identify patterns of tropane alkaloids associated with different levels of irrigation. Furthermore, we identified anatomic partitioning of tropane alkaloid isomers with pharmaceutical applications. Conclusions: Our results show that soil water availability is an effective method for maximizing the production of specific tropane alkaloids for industrial applications.
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28
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Dietz S, Herz K, Döll S, Haider S, Jandt U, Bruelheide H, Scheel D. Semi-polar root exudates in natural grassland communities. Ecol Evol 2019; 9:5526-5541. [PMID: 31160980 PMCID: PMC6540716 DOI: 10.1002/ece3.5043] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 02/07/2019] [Accepted: 02/15/2019] [Indexed: 01/03/2023] Open
Abstract
In the rhizosphere, plants are exposed to a multitude of different biotic and abiotic factors, to which they respond by exuding a wide range of secondary root metabolites. So far, it has been unknown to which degree root exudate composition is species-specific and is affected by land use, the local impact and local neighborhood under field conditions. In this study, root exudates of 10 common grassland species were analyzed, each five of forbs and grasses, in the German Biodiversity Exploratories using a combined phytometer and untargeted liquid chromatography-mass spectrometry (LC-MS) approach. Redundancy analysis and hierarchical clustering revealed a large set of semi-polar metabolites common to all species in addition to species-specific metabolites. Chemical richness and exudate composition revealed that forbs, such as Plantago lanceolata and Galium species, exuded more species-specific metabolites than grasses. Grasses instead were primarily affected by environmental conditions. In both forbs and grasses, plant functional traits had only a minor impact on plant root exudation patterns. Overall, our results demonstrate the feasibility of obtaining and untargeted profiling of semi-polar metabolites under field condition and allow a deeper view in the exudation of plants in a natural grassland community.
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Affiliation(s)
- Sophie Dietz
- Department of Stress and Developmental BiologyLeibniz Institute of Plant BiochemistryHalle (Saale)Germany
| | - Katharina Herz
- Institute of Biology/Geobotany and Botanical GardenMartin Luther University Halle‐WittenbergHalle (Saale)Germany
| | - Stefanie Döll
- Department of Stress and Developmental BiologyLeibniz Institute of Plant BiochemistryHalle (Saale)Germany
| | - Sylvia Haider
- Institute of Biology/Geobotany and Botanical GardenMartin Luther University Halle‐WittenbergHalle (Saale)Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
| | - Ute Jandt
- Institute of Biology/Geobotany and Botanical GardenMartin Luther University Halle‐WittenbergHalle (Saale)Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
| | - Helge Bruelheide
- Institute of Biology/Geobotany and Botanical GardenMartin Luther University Halle‐WittenbergHalle (Saale)Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
| | - Dierk Scheel
- Department of Stress and Developmental BiologyLeibniz Institute of Plant BiochemistryHalle (Saale)Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
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29
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He M, Hong L, Zhou Y. Multi-scale Gaussian/Haar wavelet strategies coupled with sub-window factor analysis for an accurate alignment in nontargeted metabolic profiling to enhance herbal origin discrimination capability. J Sep Sci 2019; 42:2003-2012. [PMID: 30919573 DOI: 10.1002/jssc.201801077] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 03/20/2019] [Accepted: 03/22/2019] [Indexed: 12/27/2022]
Abstract
Metabolic dataset can provide an overview of different herbal origin, which is conducted by some statistical procedures. Such results often deviate to a certain degree, due to peaks shifts in chromatographic signals. In order to solve this problem, an improved algorithm of combining sub-window factor analysis with the mass spectrum information is proposed. The algorithm uses a peak detection approach derived either from multi-scale Gaussian function or Haar wavelet to locate the peaks with different application scope; the candidate drift points at each peak are estimated by Fast Fourier transform cross correlation; Specifically, the best drift points at each candidate peaks are confirmed by sub-window factor analysis and mass spectrum information in nontargeted metabolic profiling. Finally, the peak regions were aligned against a reference chromatogram, and the non-peak regions were used linear interpolation. The chromatographic signals of 30 Bupleurum samples were aligned as an illustration of this algorithm, and they could be well distinguished using some statistical procedures. The result demonstrates that the presented method is stronger than other mass-spectra based algorithms, when facing the alignment of some co-eluted peaks.
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Affiliation(s)
- Min He
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, P. R. China
| | - Liang Hong
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, P. R. China
| | - Yu Zhou
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan, P. R. China
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30
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Perez de Souza L, Scossa F, Proost S, Bitocchi E, Papa R, Tohge T, Fernie AR. Multi-tissue integration of transcriptomic and specialized metabolite profiling provides tools for assessing the common bean (Phaseolus vulgaris) metabolome. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:1132-1153. [PMID: 30480348 PMCID: PMC6850281 DOI: 10.1111/tpj.14178] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 11/15/2018] [Accepted: 11/23/2018] [Indexed: 05/02/2023]
Abstract
Common bean (Phaseolus vulgaris L.) is an important legume species with a rich natural diversity of landraces that originated from the wild forms following multiple independent domestication events. After the publication of its genome, several resources for this relevant crop have been made available. A comprehensive characterization of specialized metabolism in P. vulgaris, however, is still lacking. In this study, we used a metabolomics approach based on liquid chromatography-mass spectrometry to dissect the chemical composition at a tissue-specific level in several accessions of common bean belonging to different gene pools. Using a combination of literature search, mass spectral interpretation, 13 C-labeling, and correlation analyses, we were able to assign chemical classes and/or putative structures for approximately 39% of all measured metabolites. Additionally, we integrated this information with transcriptomics data and phylogenetic inference from multiple legume species to reconstruct the possible metabolic pathways and identify sets of candidate genes involved in the biosynthesis of specialized metabolites. A particular focus was given to flavonoids, triterpenoid saponins and hydroxycinnamates, as they represent metabolites involved in important ecological interactions and they are also associated with several health-promoting benefits when integrated into the human diet. The data are presented here in the form of an accessible resource that we hope will set grounds for further studies on specialized metabolism in legumes.
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Affiliation(s)
| | - Federico Scossa
- Max‐Planck‐Institute of Molecular Plant PhysiologyAm Müehlenberg 1Potsdam‐Golm14476Germany
- Consiglio per la ricerca in agricoltura e l′analisi dell′economia agrariaCREA‐OFAVia di Fioranello 5200134RomeItaly
| | - Sebastian Proost
- Max‐Planck‐Institute of Molecular Plant PhysiologyAm Müehlenberg 1Potsdam‐Golm14476Germany
| | - Elena Bitocchi
- Department of Agricultural, Food, and Environmental SciencesUniversità Politecnica delle Marche60131AnconaItaly
| | - Roberto Papa
- Department of Agricultural, Food, and Environmental SciencesUniversità Politecnica delle Marche60131AnconaItaly
| | - Takayuki Tohge
- Max‐Planck‐Institute of Molecular Plant PhysiologyAm Müehlenberg 1Potsdam‐Golm14476Germany
- Graduate School of Biological SciencesNara Institute of Science and TechnologyIkoma, Nara630‐0192Japan
| | - Alisdair R. Fernie
- Max‐Planck‐Institute of Molecular Plant PhysiologyAm Müehlenberg 1Potsdam‐Golm14476Germany
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31
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Yu YJ, Zheng QX, Zhang YM, Zhang Q, Zhang YY, Liu PP, Lu P, Fan MJ, Chen QS, Bai CC, Fu HY, She Y. Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics. J Chromatogr A 2018; 1585:172-181. [PMID: 30509617 DOI: 10.1016/j.chroma.2018.11.070] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 11/06/2018] [Accepted: 11/25/2018] [Indexed: 02/06/2023]
Abstract
Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2.
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Affiliation(s)
- Yong-Jie Yu
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China; Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan, 750004, China
| | - Qing-Xia Zheng
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Yue-Ming Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China; Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan, 750004, China
| | - Qian Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China; Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan, 750004, China
| | - Yu-Ying Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China; Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan, 750004, China
| | - Ping-Ping Liu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Peng Lu
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Mei-Juan Fan
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Qian-Si Chen
- Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
| | - Chang-Cai Bai
- College of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China; Ningxia Engineering and Technology Research Center for Modernization of Hui Medicine, Ningxia Medical University, Yinchuan, 750004, China
| | - Hai-Yan Fu
- School of Pharmaceutical Sciences, South Central University for Nationalities, Wuhan, 430074, China.
| | - Yuanbin She
- Zhejiang University of Technology, Hangzhou, 310014, China.
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Wolfender JL, Nuzillard JM, van der Hooft JJJ, Renault JH, Bertrand S. Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography–High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics. Anal Chem 2018; 91:704-742. [DOI: 10.1021/acs.analchem.8b05112] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Jean-Luc Wolfender
- School of Pharmaceutical Sciences, EPGL, University of Geneva, University of Lausanne, CMU, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland
| | - Jean-Marc Nuzillard
- Institut de Chimie Moléculaire de Reims, UMR CNRS 7312, Université de Reims Champagne Ardenne, 51687 Reims Cedex 2, France
| | | | - Jean-Hugues Renault
- Institut de Chimie Moléculaire de Reims, UMR CNRS 7312, Université de Reims Champagne Ardenne, 51687 Reims Cedex 2, France
| | - Samuel Bertrand
- Groupe Mer, Molécules, Santé-EA 2160, UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes, 44035 Nantes, France
- ThalassOMICS Metabolomics Facility, Plateforme Corsaire, Biogenouest, 44035 Nantes, France
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Peters K, Worrich A, Weinhold A, Alka O, Balcke G, Birkemeyer C, Bruelheide H, Calf OW, Dietz S, Dührkop K, Gaquerel E, Heinig U, Kücklich M, Macel M, Müller C, Poeschl Y, Pohnert G, Ristok C, Rodríguez VM, Ruttkies C, Schuman M, Schweiger R, Shahaf N, Steinbeck C, Tortosa M, Treutler H, Ueberschaar N, Velasco P, Weiß BM, Widdig A, Neumann S, Dam NMV. Current Challenges in Plant Eco-Metabolomics. Int J Mol Sci 2018; 19:E1385. [PMID: 29734799 PMCID: PMC5983679 DOI: 10.3390/ijms19051385] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 04/24/2018] [Accepted: 04/25/2018] [Indexed: 12/22/2022] Open
Abstract
The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant⁻organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.
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Affiliation(s)
- Kristian Peters
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Anja Worrich
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
- UFZ-Helmholtz-Centre for Environmental Research, Department Environmental Microbiology, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Alexander Weinhold
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
| | - Oliver Alka
- Applied Bioinformatics Group, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany.
| | - Gerd Balcke
- Leibniz Institute of Plant Biochemistry, Cell and Metabolic Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Claudia Birkemeyer
- Institute of Analytical Chemistry, University of Leipzig, Linnéstr. 3, 04103 Leipzig, Germany.
| | - Helge Bruelheide
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany.
| | - Onno W Calf
- Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Sophie Dietz
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Kai Dührkop
- Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Emmanuel Gaquerel
- Centre for Organismal Studies, Heidelberg University, Im Neuenheimer Feld 360, 69120 Heidelberg, Germany.
| | - Uwe Heinig
- Weizmann Institute of Science, Faculty of Biochemistry, Department of Plant Sciences, 234 Herzl St., P.O. Box 26, Rehovot 7610001, Israel.
| | - Marlen Kücklich
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
| | - Mirka Macel
- Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Caroline Müller
- Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
| | - Yvonne Poeschl
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Informatics, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle (Saale), Germany.
| | - Georg Pohnert
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Christian Ristok
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
| | - Victor Manuel Rodríguez
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Meredith Schuman
- Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, 07745 Jena, Germany.
| | - Rabea Schweiger
- Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
| | - Nir Shahaf
- Weizmann Institute of Science, Faculty of Biochemistry, Department of Plant Sciences, 234 Herzl St., P.O. Box 26, Rehovot 7610001, Israel.
| | - Christoph Steinbeck
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Maria Tortosa
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Nico Ueberschaar
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Pablo Velasco
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Brigitte M Weiß
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
| | - Anja Widdig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
- Research Group of Primate Kin Selection, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
| | - Nicole M van Dam
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
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Han L, Zhang YM, Song JJ, Fan MJ, Yu YJ, Liu PP, Zheng QX, Chen QS, Bai CC, Sun T, She YB. Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability. J Chromatogr A 2018; 1541:12-20. [DOI: 10.1016/j.chroma.2018.02.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 01/23/2018] [Accepted: 02/07/2018] [Indexed: 10/18/2022]
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Advances in computational metabolomics and databases deepen the understanding of metabolisms. Curr Opin Biotechnol 2018; 54:10-17. [PMID: 29413746 DOI: 10.1016/j.copbio.2018.01.008] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Revised: 01/06/2018] [Accepted: 01/09/2018] [Indexed: 01/13/2023]
Abstract
Mass spectrometry (MS)-based metabolomics is the popular platform for metabolome analyses. Computational techniques for the processing of MS raw data, for example, feature detection, peak alignment, and the exclusion of false-positive peaks, have been established. The next stage of untargeted metabolomics would be to decipher the mass fragmentation of small molecules for the global identification of human-, animal-, plant-, and microbiota metabolomes, resulting in a deeper understanding of metabolisms. This review is an update on the latest computational metabolomics including known/expected structure databases, chemical ontology classifications, and mass spectrometry cheminformatics for the interpretation of mass fragmentations and for the elucidation of unknown metabolites. The importance of metabolome 'databases' and 'repositories' is also discussed because novel biological discoveries are often attributable to the accumulation of data, to relational databases, and to their statistics. Lastly, a practical guide for metabolite annotations is presented as the summary of this review.
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Godzien J, Gil de la Fuente A, Otero A, Barbas C. Metabolite Annotation and Identification. COMPREHENSIVE ANALYTICAL CHEMISTRY 2018. [DOI: 10.1016/bs.coac.2018.07.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Depke T, Franke R, Brönstrup M. Clustering of MS2 spectra using unsupervised methods to aid the identification of secondary metabolites from Pseudomonas aeruginosa. J Chromatogr B Analyt Technol Biomed Life Sci 2017. [DOI: 10.1016/j.jchromb.2017.06.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Probing Protein Glycation by Chromatography and Mass Spectrometry: Analysis of Glycation Adducts. Int J Mol Sci 2017; 18:ijms18122557. [PMID: 29182540 PMCID: PMC5751160 DOI: 10.3390/ijms18122557] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2017] [Revised: 11/26/2017] [Accepted: 11/27/2017] [Indexed: 12/14/2022] Open
Abstract
Glycation is a non-enzymatic post-translational modification of proteins, formed by the reaction of reducing sugars and α-dicarbonyl products of their degradation with amino and guanidino groups of proteins. Resulted early glycation products are readily involved in further transformation, yielding a heterogeneous group of advanced glycation end products (AGEs). Their formation is associated with ageing, metabolic diseases, and thermal processing of foods. Therefore, individual glycation adducts are often considered as the markers of related pathologies and food quality. In this context, their quantification in biological and food matrices is required for diagnostics and establishment of food preparation technologies. For this, exhaustive protein hydrolysis with subsequent amino acid analysis is the strategy of choice. Thereby, multi-step enzymatic digestion procedures ensure good recoveries for the most of AGEs, whereas tandem mass spectrometry (MS/MS) in the multiple reaction monitoring (MRM) mode with stable isotope dilution or standard addition represents “a gold standard” for their quantification. Although the spectrum of quantitatively assessed AGE structures is continuously increases, application of untargeted profiling techniques for identification of new products is desired, especially for in vivo characterization of anti-glycative systems. Thereby, due to a high glycative potential of plant metabolites, more attention needs to be paid on plant-derived AGEs.
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Fu HY, Guo XM, Zhang YM, Song JJ, Zheng QX, Liu PP, Lu P, Chen QS, Yu YJ, She Y. AntDAS: Automatic Data Analysis Strategy for UPLC–QTOF-Based Nontargeted Metabolic Profiling Analysis. Anal Chem 2017; 89:11083-11090. [DOI: 10.1021/acs.analchem.7b03160] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Hai-Yan Fu
- School
of Pharmaceutical Sciences, South Central University for Nationalities, Wuhan 430074, China
| | - Xiao-Ming Guo
- School
of Pharmaceutical Sciences, South Central University for Nationalities, Wuhan 430074, China
| | | | - Jing-Jing Song
- Ningxia Institute of Cultural Relics and Archeology, Yinchuan 750001, China
| | - Qing-Xia Zheng
- China
Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Ping-Ping Liu
- China
Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Peng Lu
- China
Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Qian-Si Chen
- China
Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | | | - Yuanbin She
- ZhengJiang University of Technology, Hangzhou 310014, China
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Abstract
Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.
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Affiliation(s)
- Biswapriya B Misra
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Johannes F Fahrmann
- Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, TX, USA
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van der Hooft JJJ, Wandy J, Young F, Padmanabhan S, Gerasimidis K, Burgess KEV, Barrett MP, Rogers S. Unsupervised Discovery and Comparison of Structural Families Across Multiple Samples in Untargeted Metabolomics. Anal Chem 2017. [PMID: 28621528 PMCID: PMC5524435 DOI: 10.1021/acs.analchem.7b01391] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
![]()
In
untargeted metabolomics
approaches, the inability to structurally
annotate relevant features and map them to biochemical pathways is
hampering the full exploitation of many metabolomics experiments.
Furthermore, variable metabolic content across samples result in sparse
feature matrices that are statistically hard to handle. Here, we introduce
MS2LDA+ that tackles both above-mentioned problems. Previously, we
presented MS2LDA, which extracts biochemically relevant molecular
substructures (“Mass2Motifs”) from a collection of fragmentation
spectra as sets of co-occurring molecular fragments and neutral losses,
thereby recognizing building blocks of metabolomics. Here, we extend
MS2LDA to handle multiple metabolomics experiments in one analysis,
resulting in MS2LDA+. By linking Mass2Motifs across samples, we expose
the variability in prevalence of structurally related metabolite families.
We validate the differential prevalence of substructures between two
distinct samples groups and apply it to fecal samples. Subsequently,
within one sample group of urines, we rank the Mass2Motifs based on
their variance to assess whether xenobiotic-derived substructures
are among the most-variant Mass2Motifs. Indeed, we could ascribe 22
out of the 30 most-variant Mass2Motifs to xenobiotic-derived substructures
including paracetamol/acetaminophen mercapturate and dimethylpyrogallol.
In total, we structurally characterized 101 Mass2Motifs with biochemically
or chemically relevant substructures. Finally, we combined the discovered
metabolite families with full scan feature intensity information to
obtain insight into core metabolites present in most samples and rare
metabolites present in small subsets now linked through their common
substructures. We conclude that by biochemical grouping of metabolites
across samples MS2LDA+ aids in structural annotation of metabolites
and guides prioritization of analysis by using Mass2Motif prevalence.
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Affiliation(s)
- Justin J J van der Hooft
- Glasgow Polyomics, University of Glasgow , Glasgow G61 1HQ, United Kingdom.,Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow , Glasgow G12 8QQ, United Kingdom
| | - Joe Wandy
- Glasgow Polyomics, University of Glasgow , Glasgow G61 1HQ, United Kingdom
| | - Francesca Young
- Glasgow Polyomics, University of Glasgow , Glasgow G61 1HQ, United Kingdom
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow , Glasgow G12 8QQ, United Kingdom
| | - Konstantinos Gerasimidis
- Human Nutrition, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow , New Lister Building, Glasgow Royal Infirmary, Glasgow G31 2ER, United Kingdom
| | - Karl E V Burgess
- Glasgow Polyomics, University of Glasgow , Glasgow G61 1HQ, United Kingdom
| | - Michael P Barrett
- Glasgow Polyomics, University of Glasgow , Glasgow G61 1HQ, United Kingdom.,Wellcome Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation, University of Glasgow , Glasgow G12 8TA, United Kingdom
| | - Simon Rogers
- Glasgow Polyomics, University of Glasgow , Glasgow G61 1HQ, United Kingdom.,School of Computing Science, University of Glasgow , Glasgow G12 8RZ, United Kingdom
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Perez de Souza L, Naake T, Tohge T, Fernie AR. From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics. Gigascience 2017; 6:1-20. [PMID: 28520864 PMCID: PMC5499862 DOI: 10.1093/gigascience/gix037] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 05/08/2017] [Accepted: 05/12/2017] [Indexed: 01/19/2023] Open
Abstract
The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.
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Affiliation(s)
- Leonardo Perez de Souza
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Thomas Naake
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Takayuki Tohge
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
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Bjornson M, Balcke GU, Xiao Y, de Souza A, Wang JZ, Zhabinskaya D, Tagkopoulos I, Tissier A, Dehesh K. Integrated omics analyses of retrograde signaling mutant delineate interrelated stress-response strata. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2017; 91:70-84. [PMID: 28370892 PMCID: PMC5488868 DOI: 10.1111/tpj.13547] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 03/15/2017] [Accepted: 03/20/2017] [Indexed: 05/19/2023]
Abstract
To maintain homeostasis in the face of intrinsic and extrinsic insults, cells have evolved elaborate quality control networks to resolve damage at multiple levels. Interorganellar communication is a key requirement for this maintenance, however the underlying mechanisms of this communication have remained an enigma. Here we integrate the outcome of transcriptomic, proteomic, and metabolomics analyses of genotypes including ceh1, a mutant with constitutively elevated levels of both the stress-specific plastidial retrograde signaling metabolite methyl-erythritol cyclodiphosphate (MEcPP) and the defense hormone salicylic acid (SA), as well as the high MEcPP but SA deficient genotype ceh1/eds16, along with corresponding controls. Integration of multi-omic analyses enabled us to delineate the function of MEcPP from SA, and expose the compartmentalized role of this retrograde signaling metabolite in induction of distinct but interdependent signaling cascades instrumental in adaptive responses. Specifically, here we identify strata of MEcPP-sensitive stress-response cascades, among which we focus on selected pathways including organelle-specific regulation of jasmonate biosynthesis; simultaneous induction of synthesis and breakdown of SA; and MEcPP-mediated alteration of cellular redox status in particular glutathione redox balance. Collectively, these integrated multi-omic analyses provided a vehicle to gain an in-depth knowledge of genome-metabolism interactions, and to further probe the extent of these interactions and delineate their functional contributions. Through this approach we were able to pinpoint stress-mediated transcriptional and metabolic signatures and identify the downstream processes modulated by the independent or overlapping functions of MEcPP and SA in adaptive responses.
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Affiliation(s)
- Marta Bjornson
- Dept. of Plant Biology, University of California, Davis, CA 95616
- Dept. of Plant Sciences, University of California, Davis, CA 95616
| | | | - Yanmei Xiao
- Dept. of Plant Biology, University of California, Davis, CA 95616
| | - Amancio de Souza
- Dept. of Plant Biology, University of California, Davis, CA 95616
| | - Jin-Zheng Wang
- Dept. of Plant Biology, University of California, Davis, CA 95616
| | - Dina Zhabinskaya
- Dept. of Computer Science, University of California, Davis, CA 95616
| | - Ilias Tagkopoulos
- Dept. of Cell and Metabolic Biology, Leibniz-Institute of Plant Biochemistry, Halle, Germany
| | - Alain Tissier
- Dept. of Physics, University of California, Davis, CA 95616
| | - Katayoon Dehesh
- Dept. of Plant Biology, University of California, Davis, CA 95616
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Zhang Q, Ford LA, Evans AM, Toal DR. Structure elucidation of metabolite x17299 by interpretation of mass spectrometric data. Metabolomics 2017; 13:92. [PMID: 28706470 PMCID: PMC5486616 DOI: 10.1007/s11306-017-1231-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 06/19/2017] [Indexed: 12/11/2022]
Abstract
INTRODUCTION A major bottleneck in metabolomic studies is metabolite identification from accurate mass spectrometric data. Metabolite x17299 was identified in plasma as an unknown in a metabolomic study using a compound-centric approach where the associated ion features of the compound were used to determine the true molecular mass. OBJECTIVES The aim of this work is to elucidate the chemical structure of x17299, a new compound by de novo interpretation of mass spectrometric data. METHODS An Orbitrap Elite mass spectrometer was used for acquisition of mass spectra up to MS4 at high resolution. Synthetic standards of N,N,N-trimethyl-l-alanyl-l-proline betaine (l,l-TMAP), a diastereomer, and an enantiomer were chemically prepared. RESULTS The planar structure of x17299 was successfully proposed by de novo mechanistic interpretation of mass spectrometric data without any laborious purification and nuclear magnetic resonance spectroscopic analysis. The proposed structure was verified by deuterium exchanged mass spectrometric analysis and confirmed by comparison to a synthetic standard. Relative configuration of x17299 was determined by direct chromatographic comparison to a pair of synthetic diastereomers. Absolute configuration was assigned after derivatization of x17299 with a chiral auxiliary group followed by its chromatographic comparison to a pair of synthetic standards. CONCLUSION The chemical structure of metabolite x17299 was determined to be l,l-TMAP.
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Affiliation(s)
- Qibo Zhang
- Metabolon, Inc., 617 Davis Drive, Suite 400, Morrisville, NC 27560 USA
| | - Lisa A. Ford
- Metabolon, Inc., 617 Davis Drive, Suite 400, Morrisville, NC 27560 USA
| | - Anne M. Evans
- Metabolon, Inc., 617 Davis Drive, Suite 400, Morrisville, NC 27560 USA
| | - Douglas R. Toal
- Metabolon, Inc., 617 Davis Drive, Suite 400, Morrisville, NC 27560 USA
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Meier R, Ruttkies C, Treutler H, Neumann S. Bioinformatics can boost metabolomics research. J Biotechnol 2017; 261:137-141. [PMID: 28554829 DOI: 10.1016/j.jbiotec.2017.05.018] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Revised: 05/10/2017] [Accepted: 05/16/2017] [Indexed: 11/18/2022]
Abstract
Metabolomics is the modern term for the field of small molecule research in biology and biochemistry. Currently, metabolomics is undergoing a transition where the classic analytical chemistry is combined with modern cheminformatics and bioinformatics methods, paving the way for large-scale data analysis. We give some background on past developments, highlight current state-of-the-art approaches, and give a perspective on future requirements.
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Affiliation(s)
- René Meier
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany; German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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