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López-Tubau JM, Laibach N, Burciaga-Monge A, Alseekh S, Deng C, Fernie AR, Altabella T, Ferrer A. Differential impact of impaired steryl ester biosynthesis on the metabolome of tomato fruits and seeds. PHYSIOLOGIA PLANTARUM 2025; 177:e70022. [PMID: 39710490 DOI: 10.1111/ppl.70022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2024] [Revised: 12/03/2024] [Accepted: 12/10/2024] [Indexed: 12/24/2024]
Abstract
Steryl esters (SE) are a storage pool of sterols that accumulates in cytoplasmic lipid droplets and helps to maintain plasma membrane sterol homeostasis throughout plant growth and development. Ester formation in plant SE is catalyzed by phospholipid:sterol acyltransferase (PSAT) and acyl-CoA:sterol acyltransferase (ASAT), which transfer long-chain fatty acid groups to free sterols from phospholipids and acyl-CoA, respectively. Comparative mass spectrometry-based metabolomic analysis between ripe fruits and seeds of a tomato (Solanum lycopersicum cv Micro-Tom) mutant lacking functional PSAT and ASAT enzymes (slasat1xslpsat1) shows that disruption of SE biosynthesis has a differential impact on the metabolome of these organs, including changes in the composition of free and glycosylated sterols. Significant perturbations were observed in the fruit lipidome in contrast to the mild effect detected in the lipidome of seeds. A contrasting response was also observed in phenylpropanoid metabolism, which is down-regulated in fruits and appears to be stimulated in seeds. Comparison of global metabolic changes using volcano plot analysis suggests that disruption of SE biosynthesis favours a general state of metabolic activation that is more evident in seeds than fruits. Interestingly, there is an induction of autophagy in both tissues, which may contribute along with other metabolic changes to the phenotypes of early seed germination and enhanced fruit tolerance to Botrytis cinerea displayed by the slasat1xslpsat1 mutant. The results of this study reveal unreported connections between SE metabolism and the metabolic status of plant cells and lay the basis for further studies aimed at elucidating the mechanisms underlying the observed effects.
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Affiliation(s)
- Joan Manel López-Tubau
- Plant Synthetic Biology and Metabolic Engineering Program, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Cerdanyola, Barcelona, Spain
| | - Natalie Laibach
- Plant Synthetic Biology and Metabolic Engineering Program, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Cerdanyola, Barcelona, Spain
- Hochshule Rhein-Waal. Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Kleve, Germany
| | - Alma Burciaga-Monge
- Plant Synthetic Biology and Metabolic Engineering Program, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Cerdanyola, Barcelona, Spain
| | - Saleh Alseekh
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Cuiyun Deng
- Plant Synthetic Biology and Metabolic Engineering Program, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Cerdanyola, Barcelona, Spain
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Teresa Altabella
- Plant Synthetic Biology and Metabolic Engineering Program, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Cerdanyola, Barcelona, Spain
- Department of Biology, Healthcare and the Environment, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
| | - Albert Ferrer
- Plant Synthetic Biology and Metabolic Engineering Program, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Cerdanyola, Barcelona, Spain
- Department of Biochemistry and Physiology, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain
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Fontanet‐Manzaneque JB, Laibach N, Herrero‐García I, Coleto‐Alcudia V, Blasco‐Escámez D, Zhang C, Orduña L, Alseekh S, Miller S, Bjarnholt N, Fernie AR, Matus JT, Caño‐Delgado AI. Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:3406-3423. [PMID: 39325724 PMCID: PMC11606431 DOI: 10.1111/pbi.14461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/07/2024] [Accepted: 08/22/2024] [Indexed: 09/28/2024]
Abstract
Drought is a critical issue in modern agriculture; therefore, there is a need to create crops with drought resilience. The complexity of plant responses to abiotic stresses, particularly in the field of brassinosteroid (BR) signalling, has been the subject of extensive research. In this study, we unveil compelling insights indicating that the BRASSINOSTEROID-INSENSITIVE 1 (BRI1) receptor in Arabidopsis and Sorghum plays a critical role as a negative regulator of drought responses. Introducing untargeted mutation in the sorghum BRI1 receptor (SbBRI1) effectively enhances the plant's ability to withstand osmotic and drought stress. Through DNA Affinity Purification sequencing (DAP-seq), we show that the sorghum BRI1-EMS-SUPPRESSOR 1 (SbBES1) transcription factor, a downstream player of the BR signalling, binds to a conserved G-box binding motif, and it is responsible for regulating BR homeostasis, as its Arabidopsis ortholog AtBES1. We further characterized the drought tolerance of sorghum bri1 mutants and decipher SbBES1-mediated regulation of phenylpropanoid pathway. Our findings suggest that SbBRI1 signalling serves a dual purpose: under normal conditions, it regulates lignin biosynthesis by SbBES1, but during drought conditions, BES1 becomes less active, allowing the activation of the flavonoid pathway. This adaptive shift improves the photosynthetic rate and photoprotection, reinforcing crop adaptation to drought.
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Affiliation(s)
- Juan B. Fontanet‐Manzaneque
- Department of Molecular GeneticsCentre for Research in Agricultural Genomics (CRAG) CSIC‐IRTA‐UAB‐UBBarcelonaSpain
| | - Natalie Laibach
- Department of Molecular GeneticsCentre for Research in Agricultural Genomics (CRAG) CSIC‐IRTA‐UAB‐UBBarcelonaSpain
- Present address:
Rhine‐Waal University of Applied Science, University of Copenhagen, Life Science FacultyKleveDenmark
| | - Iván Herrero‐García
- Department of Molecular GeneticsCentre for Research in Agricultural Genomics (CRAG) CSIC‐IRTA‐UAB‐UBBarcelonaSpain
| | - Veredas Coleto‐Alcudia
- Department of Molecular GeneticsCentre for Research in Agricultural Genomics (CRAG) CSIC‐IRTA‐UAB‐UBBarcelonaSpain
| | - David Blasco‐Escámez
- Department of Molecular GeneticsCentre for Research in Agricultural Genomics (CRAG) CSIC‐IRTA‐UAB‐UBBarcelonaSpain
- Present address:
VIB‐UGent Center for Plant Systems BiologyGhenteBelgium
| | - Chen Zhang
- Institute for Integrative Systems Biology (I2SysBio)Universitat de València‐CSICPaternaValenciaSpain
| | - Luis Orduña
- Institute for Integrative Systems Biology (I2SysBio)Universitat de València‐CSICPaternaValenciaSpain
| | - Saleh Alseekh
- Max‐Planck‐Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
- Center of Plant Systems Biology and BiotechnologyPlovdivBulgaria
| | - Sara Miller
- Copenhagen Plant Science Center, Department of Plant and Environmental SciencesUniversity of CopenhagenFrederiksbergDenmark
| | - Nanna Bjarnholt
- Copenhagen Plant Science Center, Department of Plant and Environmental SciencesUniversity of CopenhagenFrederiksbergDenmark
| | - Alisdair R. Fernie
- Max‐Planck‐Institute of Molecular Plant PhysiologyPotsdam‐GolmGermany
- Center of Plant Systems Biology and BiotechnologyPlovdivBulgaria
| | - José Tomás Matus
- Institute for Integrative Systems Biology (I2SysBio)Universitat de València‐CSICPaternaValenciaSpain
| | - Ana I. Caño‐Delgado
- Department of Molecular GeneticsCentre for Research in Agricultural Genomics (CRAG) CSIC‐IRTA‐UAB‐UBBarcelonaSpain
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3
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A Flexible Tool to Correct Superimposed Mass Isotopologue Distributions in GC-APCI-MS Flux Experiments. Metabolites 2022; 12:metabo12050408. [PMID: 35629912 PMCID: PMC9144802 DOI: 10.3390/metabo12050408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/14/2022] [Accepted: 04/28/2022] [Indexed: 02/05/2023] Open
Abstract
The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer-based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.
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Triangular-wave controlled amplitude-modulated flow analysis for extending dynamic range to saturated signals. ANAL SCI 2022; 38:795-802. [PMID: 35298795 DOI: 10.1007/s44211-022-00097-4] [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: 12/10/2021] [Accepted: 02/04/2022] [Indexed: 11/01/2022]
Abstract
We have applied our amplitude-modulated flow analysis concept to extend the dynamic range to saturated analytical signals. Sample solution, the flow rate FS of which is periodically varied with a triangular control signal Vc, is merged with a reagent solution delivered at a constant flow rate FR. While the total flow rate FT is kept constant, a diluent with FT-(FS + FR) flow rate is aspirated from the third channel. The analytical signal Vd obtained downstream is processed by fast Fourier transform to obtain the amplitudes of the wave components in Vd. When the sample concentration CS is low, Vd shows a triangular profile like Vc; the sum of the amplitudes ΣA is proportional to CS. When CS is high, Vd shows a trapezoidal profile because of the Vd saturation. A linear calibration curve can also be obtained for such saturated signals by plotting CSΣA against CS. The proof of the concept is validated by applying it to spectrophotometric determinations of a dye (Methylene Blue) and colored complexes of Fe2+ - phenanthroline and Fe3+ - Tiron.
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Defining Blood Plasma and Serum Metabolome by GC-MS. Metabolites 2021; 12:metabo12010015. [PMID: 35050137 PMCID: PMC8779220 DOI: 10.3390/metabo12010015] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/18/2021] [Accepted: 12/21/2021] [Indexed: 01/04/2023] Open
Abstract
Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.
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Xue J, Derks RJ, Webb W, Billings EM, Aisporna A, Giera M, Siuzdak G. Single Quadrupole Multiple Fragment Ion Monitoring Quantitative Mass Spectrometry. Anal Chem 2021; 93:10879-10889. [PMID: 34313111 PMCID: PMC8762722 DOI: 10.1021/acs.analchem.1c01246] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Single quadrupole mass spectrometry (MS) with enhanced in-source multiple fragment ion monitoring was designed to perform high sensitivity quantitative mass analyses. Enhanced in-source fragmentation amplifies fragmentation from traditional soft electrospray ionization producing fragment ions that have been found to be identical to those generated in tandem MS. We have combined enhanced in-source fragmentation data with criteria established by the European Union Commission Directive 2002/657/EC for electron ionization single quadrupole quantitative analysis to perform quantitative analyses. These experiments were performed on multiple types of complex samples that included a mixture of 50 standards, as well as cell and plasma extracts. The dynamic range for these quantitative analyses was comparable to triple quadrupole multiple reaction monitoring (MRM) analyses at up to 5 orders of magnitude with the cell and plasma extracts showing similar matrix effects across both platforms. Amino acid and fatty acid measurements performed from certified NIST 1950 plasma with isotopically labeled standards demonstrated accuracy in the range of 91-110% for the amino acids, 76-129% for the fatty acids, and good precision (coefficient of variation <10%). To enhance specificity, a newly developed correlated ion monitoring algorithm was designed to facilitate these analyses. This algorithm autonomously processes, aligns, filters, and compiles multiple ions within one chromatogram enabling both precursor and in-source fragment ions to be correlated within a single chromatogram, also enabling the detection of coeluting species based on precursor and fragment ion ratios. Single quadrupole instrumentation can provide MRM level quantitative performance by monitoring/correlating precursor and fragment ions facilitating high sensitivity analysis on existing single quadrupole instrumentation that are generally inexpensive, easy to operate, and technically less complex.
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Affiliation(s)
- Jingchuan Xue
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Rico J.E. Derks
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2333ZA Leiden, Netherlands
| | - William Webb
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Elizabeth M. Billings
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Aries Aisporna
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Martin Giera
- Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2333ZA Leiden, Netherlands
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
- Departments of Chemistry, Molecular, and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, United States
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Systematic Identification of MACC1-Driven Metabolic Networks in Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13050978. [PMID: 33652667 PMCID: PMC7956336 DOI: 10.3390/cancers13050978] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/16/2021] [Accepted: 02/21/2021] [Indexed: 11/25/2022] Open
Abstract
Simple Summary We aimed at the systematic identification of MACC1-driven metabolic networks in colorectal cancer. By this systematic analysis, our studies revealed new insights into MACC1-caused metabolomics phenotypes: (i) MACC1 fosters metastasis by rewiring glucose and glutamine metabolism, (ii) MACC1 increases glucose use by enhanced surface GLUT1; (iii) MACC1 increases glutamine and pyruvate use by enhanced uptake, and (iv) MACC1 reduces glutamine flux but has minor effects on pyruvate flux. Therefore, MACC1 is an important regulator of cancer metabolism. Abstract MACC1 is a prognostic and predictive metastasis biomarker for more than 20 solid cancer entities. However, its role in cancer metabolism is not sufficiently explored. Here, we report on how MACC1 impacts the use of glucose, glutamine, lactate, pyruvate and fatty acids and show the comprehensive analysis of MACC1-driven metabolic networks. We analyzed concentration-dependent changes in nutrient use, nutrient depletion, metabolic tracing employing 13C-labeled substrates, and in vivo studies. We found that MACC1 permits numerous effects on cancer metabolism. Most of those effects increased nutrient uptake. Furthermore, MACC1 alters metabolic pathways by affecting metabolite production or turnover from metabolic substrates. MACC1 supports use of glucose, glutamine and pyruvate via their increased depletion or altered distribution within metabolic pathways. In summary, we demonstrate that MACC1 is an important regulator of metabolism in cancer cells.
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Niu Y, Liu J, Yang R, Zhang J, Shao B. Atmospheric pressure chemical ionization source as an advantageous technique for gas chromatography-tandem mass spectrometry. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.116053] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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9
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Kotani A, Hakamata H, Hayashi Y. An automated assessment system of limits of detection and quantitation in gradient high-performance liquid chromatography with ultraviolet detection. J Chromatogr A 2020; 1621:461077. [DOI: 10.1016/j.chroma.2020.461077] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 03/18/2020] [Accepted: 03/24/2020] [Indexed: 01/16/2023]
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10
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An automated system for predicting detection limit and precision profile from a chromatogram. J Chromatogr A 2020; 1612:460644. [PMID: 31676091 DOI: 10.1016/j.chroma.2019.460644] [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: 07/29/2019] [Revised: 10/13/2019] [Accepted: 10/19/2019] [Indexed: 11/22/2022]
Abstract
This paper presents a basic model of an automated system for predicting the detection limit and precision profile (plot of relative standard deviation (RSD) of measurements against concentration) in chromatography. The fundamental assumption is that the major source of response errors at low sample concentrations is background noise and at high concentrations, it is the volumes injected into an HPLC system by a sample injector. The noise is approximated by the mixed random processes of the first order autoregressive process AR(1) and white noise. The research procedures are: (1) the description of the standard deviation (SD) of measurements in terms of the parameters of the mixed random processes; (2) the algorithm for the parameter estimation of the mixed processes from actual background noise; (3) the mathematical distinction between noise and signal in a chromatogram. When compounds are chromatographically separated, each obtained signal is given the detection limit and precision profile on laboratory-made software. A file of a chromatogram is the only requirement for the theoretical prediction of measurement uncertainty and therefore the repeated measurements of real samples can be dispensed with. The theoretically predicted RSDs are verified by comparing them with the statistical RSDs obtained by repeated measurements. Signal shapes on noise are illustrated at the detection limit and quantitation limit, the signal-to-noise ratios of which are close to the widely adopted values, 3 and 10, respectively.
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11
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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: 8.5] [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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Beale DJ, Pinu FR, Kouremenos KA, Poojary MM, Narayana VK, Boughton BA, Kanojia K, Dayalan S, Jones OAH, Dias DA. Review of recent developments in GC-MS approaches to metabolomics-based research. Metabolomics 2018; 14:152. [PMID: 30830421 DOI: 10.1007/s11306-018-1449-2] [Citation(s) in RCA: 246] [Impact Index Per Article: 35.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 11/08/2018] [Indexed: 12/19/2022]
Abstract
BACKGROUND Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available. AIM OF REVIEW This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics. KEY SCIENTIFIC CONCEPTS OF REVIEW The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.
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Affiliation(s)
- David J Beale
- Land and Water, Commonwealth Scientific & Industrial Research Organization (CSIRO), P.O. Box 2583, Brisbane, QLD, 4001, Australia.
| | - Farhana R Pinu
- The New Zealand Institute for Plant & Food Research Limited, Private Bag 92169, Auckland, 1142, New Zealand
| | - Konstantinos A Kouremenos
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, 3010, Australia
- Trajan Scientific and Medical, 7 Argent Pl, Ringwood, 3134, Australia
| | - Mahesha M Poojary
- Chemistry Section, School of Science and Technology, University of Camerino, via S. Agostino 1, 62032, Camerino, Italy
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, 1958, Frederiksberg C, Denmark
| | - Vinod K Narayana
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, 3010, Australia
| | - Berin A Boughton
- Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, 3010, Australia
| | - Komal Kanojia
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, 3010, Australia
| | - Saravanan Dayalan
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, 3010, Australia
| | - Oliver A H Jones
- Australian Centre for Research on Separation Science (ACROSS), School of Science, RMIT University, GPO Box 2476, Melbourne, 3001, Australia
| | - Daniel A Dias
- School of Health and Biomedical Sciences, Discipline of Laboratory Medicine, RMIT University, PO Box 71, Bundoora, 3083, Australia.
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Hoffmann F, Jaeger C, Bhattacharya A, Schmitt CA, Lisec J. Nontargeted Identification of Tracer Incorporation in High-Resolution Mass Spectrometry. Anal Chem 2018; 90:7253-7260. [PMID: 29799187 DOI: 10.1021/acs.analchem.8b00356] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
"Fluxomics" refers to the systematic analysis of metabolic fluxes in a biological system and may uncover novel dynamic properties of metabolism that remain undetected in conventional metabolomic approaches. In labeling experiments, tracer molecules are used to track changes in the isotopologue distribution of metabolites, which allows one to estimate fluxes in the metabolic network. Because unidentified compounds cannot be mapped on pathways, they are often neglected in labeling experiments. However, using recent developments in de novo annotation may allow to harvest the information present in these compounds if they can be identified. Here, we present a novel tool (HiResTEC) to detect tracer incorporation in high-resolution mass spectrometry data sets. The software automatically extracts a comprehensive, nonredundant list of all compounds showing more than 1% tracer incorporation in a nontargeted fashion. We explain and show in an example data set how mass precision and other filter heuristics, calculated on the raw data, can efficiently be used to reduce redundancy and noninformative signals by 95%. Ultimately, this allows to quickly investigate any labeling experiment for a complete set of labeled compounds (here 149) with acceptable false positive rates. We further re-evaluate a published data set from liquid chromatography-electrospray ionization (LC-ESI) to demonstrate broad applicability of our tool and emphasize importance of quality control (QC) tests. HiResTEC is provided as a package in the open source software framework R and is freely available on CRAN.
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Affiliation(s)
- Friederike Hoffmann
- Charité-Universitätsmedizin Berlin , Medical Department of Hematology, Oncology, and Tumor Immunology and Molekulares Krebsforschungszentrum (MKFZ) , Augustenburger Platz 1 , 13353 Berlin , Germany
| | - Carsten Jaeger
- Charité-Universitätsmedizin Berlin , Medical Department of Hematology, Oncology, and Tumor Immunology and Molekulares Krebsforschungszentrum (MKFZ) , Augustenburger Platz 1 , 13353 Berlin , Germany.,Berlin Institute of Health (BIH) , Anna-Louisa-Karsch 2 , 10178 Berlin , Germany
| | - Animesh Bhattacharya
- Charité-Universitätsmedizin Berlin , Medical Department of Hematology, Oncology, and Tumor Immunology and Molekulares Krebsforschungszentrum (MKFZ) , Augustenburger Platz 1 , 13353 Berlin , Germany
| | - Clemens A Schmitt
- Charité-Universitätsmedizin Berlin , Medical Department of Hematology, Oncology, and Tumor Immunology and Molekulares Krebsforschungszentrum (MKFZ) , Augustenburger Platz 1 , 13353 Berlin , Germany.,Berlin Institute of Health (BIH) , Anna-Louisa-Karsch 2 , 10178 Berlin , Germany.,Max-Delbrück-Center for Molecular Medicine (MDC) , Robert-Rössle-Straße 10 , 13125 Berlin , Germany
| | - Jan Lisec
- Federal Institute for Materials Research and Testing (BAM) , Division 1.7 Analytical Chemistry , Richard-Willstätter-Straße 11 , 12489 Berlin , Germany
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14
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Kaufmann A, Walker S. Comparison of linear intrascan and interscan dynamic ranges of Orbitrap and ion-mobility time-of-flight mass spectrometers. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2017; 31:1915-1926. [PMID: 28875592 DOI: 10.1002/rcm.7981] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 08/28/2017] [Accepted: 08/31/2017] [Indexed: 06/07/2023]
Abstract
RATIONALE The linear intrascan and interscan dynamic ranges of mass spectrometers are important in metabolome and residue analysis. A large linear dynamic range is mandatory if both low- and high-abundance ions have to be detected and quantitated in heavy matrix samples. These performance criteria, as provided by modern high-resolution mass spectrometry (HRMS), were systematically investigated. METHODS The comparison included two generations of Orbitraps, and an ion mobility quadrupole time-of-flight (QTOF) system In addition, different scan modes, as provided by the utilized instruments, were investigated. Calibration curves of different compounds covering a concentration range of five orders of magnitude were measured to evaluate the linear interscan dynamic range. The linear intrascan dynamic range and the resulting mass accuracy were evaluated by repeating these measurements in the presence of a very intense background. RESULTS Modern HRMS instruments can show linear dynamic ranges of five orders of magnitude. Often, however, the linear dynamic range is limited by the detection capability (sensitivity and selectivity) and by the electrospray ionization. Orbitraps, as opposed to TOF instruments, show a reduced intrascan dynamic range. This is due to the limited C-trap and Orbitrap capacity. The tested TOF instrument shows poorer mass accuracies than the Orbitraps. In contrast, hyphenation with an ion-mobility device seems not to affect the linear dynamic range. CONCLUSIONS The linear dynamic range of modern HRMS instrumentation has been significantly improved. This also refers to the virtual absence of systematic mass shifts at high ion abundances. The intrascan dynamic range of the current Orbitrap technology may still be a limitation when analyzing complex matrix extracts. On the other hand, the linear dynamic range is not only limited by the detector technology, but can also be shortened by peripheral devices, where the ionization and transfer of ions take place.
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Affiliation(s)
- Anton Kaufmann
- Official Food Control Authority, Fehrenstrasse 15, 8032, Zürich, Switzerland
| | - Stephan Walker
- Official Food Control Authority, Fehrenstrasse 15, 8032, Zürich, Switzerland
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15
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Pinkerton DK, Reaser BC, Berrier KL, Synovec RE. Determining the Probability of Achieving a Successful Quantitative Analysis for Gas Chromatography–Mass Spectrometry. Anal Chem 2017; 89:9926-9933. [DOI: 10.1021/acs.analchem.7b02230] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- David K. Pinkerton
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Brooke C. Reaser
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Kelsey L. Berrier
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Robert E. Synovec
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
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16
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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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17
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Kadjo AF, Liao H, Dasgupta PK, Kraiczek KG. Width Based Characterization of Chromatographic Peaks: Beyond Height and Area. Anal Chem 2017; 89:3893-3900. [DOI: 10.1021/acs.analchem.6b04858] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Akinde F. Kadjo
- Department of Chemistry and
Biochemistry, University of Texas at Arlington, Arlington, Texas 76019-0065, United States
| | - Hongzhu Liao
- Department of Chemistry and
Biochemistry, University of Texas at Arlington, Arlington, Texas 76019-0065, United States
| | - Purnendu K. Dasgupta
- Department of Chemistry and
Biochemistry, University of Texas at Arlington, Arlington, Texas 76019-0065, United States
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18
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Jaeger C, Hoffmann F, Schmitt CA, Lisec J. Automated Annotation and Evaluation of In-Source Mass Spectra in GC/Atmospheric Pressure Chemical Ionization-MS-Based Metabolomics. Anal Chem 2016; 88:9386-9390. [PMID: 27584561 DOI: 10.1021/acs.analchem.6b02743] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Gas chromatography using atmospheric pressure chemical ionization coupled to mass spectrometry (GC/APCI-MS) is an emerging metabolomics platform, providing much-enhanced capabilities for structural mass spectrometry as compared to traditional electron ionization (EI)-based techniques. To exploit the potential of GC/APCI-MS for more comprehensive metabolite annotation, a major bottleneck in metabolomics, we here present the novel R-based tool InterpretMSSpectrum assisting in the common task of annotating and evaluating in-source mass spectra as obtained from typical full-scan experiments. After passing a list of mass-intensity pairs, InterpretMSSpectrum locates the molecular ion (M0), fragment, and adduct peaks, calculates their most likely sum formula combination, and graphically summarizes results as an annotated mass spectrum. Using (modifiable) filter rules for the commonly used methoximated-trimethylsilylated (MeOx-TMS) derivatives, covering elemental composition, typical substructures, neutral losses, and adducts, InterpretMSSpectrum significantly reduces the number of sum formula candidates, minimizing manual effort for postprocessing candidate lists. We demonstrate the utility of InterpretMSSpectrum for 86 in-source spectra of derivatized standard compounds, in which rank-1 sum formula assignments were achieved in 84% of the cases, compared to only 63% when using mass and isotope information on the M0 alone. We further use, for the first time, automated annotation to evaluate the purity of pseudospectra generated by different metabolomics preprocessing tools, showing that automated annotation can serve as an integrative quality measure for peak picking/deconvolution methods. As an R package, InterpretMSSpectrum integrates flexibly into existing metabolomics pipelines and is freely available from CRAN ( https://cran.r-project.org/ ).
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Affiliation(s)
- Carsten Jaeger
- Charité - Universitätsmedizin Berlin , Medical Department of Hematology, Oncology, and Tumor Immunology, and Molekulares Krebsforschungszentrum (MKFZ), Augustenburger Platz 1, 13353 Berlin, Germany.,Berlin Institute of Health (BIH) , Kapelle-Ufer 2, 10117 Berlin, Germany
| | - Friederike Hoffmann
- Charité - Universitätsmedizin Berlin , Medical Department of Hematology, Oncology, and Tumor Immunology, and Molekulares Krebsforschungszentrum (MKFZ), Augustenburger Platz 1, 13353 Berlin, Germany
| | - Clemens A Schmitt
- Charité - Universitätsmedizin Berlin , Medical Department of Hematology, Oncology, and Tumor Immunology, and Molekulares Krebsforschungszentrum (MKFZ), Augustenburger Platz 1, 13353 Berlin, Germany.,Berlin Institute of Health (BIH) , Kapelle-Ufer 2, 10117 Berlin, Germany.,Max-Delbrück-Center for Molecular Medicine (MDC) , Robert-Rössle-Straße 10, 13125 Berlin, Germany
| | - Jan Lisec
- Charité - Universitätsmedizin Berlin , Medical Department of Hematology, Oncology, and Tumor Immunology, and Molekulares Krebsforschungszentrum (MKFZ), Augustenburger Platz 1, 13353 Berlin, Germany.,German Cancer Consortium, Deutsches Krebsforschungzentrum (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
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