Abstract
Plant specialized metabolites have diversified vastly over the course of plant evolution, and they are considered key players in complex interactions between plants and their environment. The chemical diversity of these metabolites has been widely explored and utilized in agriculture and crop enhancement, the food industry, and drug development, among other areas. However, the immensity of the plant metabolome can make its exploration challenging. Here we describe a protocol for exploring plant specialized metabolites that combines high-resolution mass spectrometry and computational metabolomics strategies, including molecular networking, identification of structural motifs, as well as prediction of chemical structures and metabolite classes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Weng J-K, Philippe RN, Noel JP (2012) The rise of chemodiversity in plants. Science 336:1667–1670. https://doi.org/10.1126/science.1217411
Jorge TF, Rodrigues JA, Caldana C et al (2016) Mass spectrometry-based plant metabolomics: metabolite responses to abiotic stress. Mass Spectrom Rev 35:620–649. https://doi.org/10.1002/mas.21449
Fiehn O (2002) Metabolomics the link between genotypes and phenotypes. Plant Mol Biol 48:155–171. https://doi.org/10.1023/A:1013713905833
Tsugawa H, Rai A, Saito K, Nakabayashi R (2021) Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Nat Prod Rep 38:1729–1759. https://doi.org/10.1039/D1NP00014D
Newman DJ, Cragg GM (2020) Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J Nat Prod 83:770–803. https://doi.org/10.1021/acs.jnatprod.9b01285
Ahmed N, Alam M, Saeed M et al (2021) Botanical insecticides are a non-toxic alternative to conventional pesticides in the control of insects and pests. In: Global decline of insects. IntechOpen, London, pp 1–19
Sharmeen JB, Mahomoodally FM, Zengin G, Maggi F (2021) Essential oils as natural sources of fragrance compounds for cosmetics and cosmeceuticals. Molecules 26:666. https://doi.org/10.3390/molecules26030666
Kallscheuer N, Classen T, Drepper T, Marienhagen J (2019) Production of plant metabolites with applications in the food industry using engineered microorganisms. Curr Opin Biotechnol 56:7–17. https://doi.org/10.1016/j.copbio.2018.07.008
Kessler A, Kalske A (2018) Plant secondary metabolite diversity and species interactions. Annu Rev Ecol Evol Syst 49:115. https://doi.org/10.1146/annurev-ecolsys-110617-062406
Verpoorte R, Choi YH, Mustafa NR, Kim HK (2008) Metabolomics: back to basics. Phytochem Rev 7:525–537. https://doi.org/10.1007/s11101-008-9091-7
Alseekh S, Aharoni A, Brotman Y et al (2021) Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat Methods 18:747–756. https://doi.org/10.1038/s41592-021-01197-1
Oikawa A, Otsuka T, Jikumaru Y et al (2011) Effects of freeze-drying of samples on metabolite levels in metabolome analyses. J Sep Sci 34:3561–3567. https://doi.org/10.1002/jssc.201100466
Kim HK, Verpoorte R (2010) Sample preparation for plant metabolomics. Phytochem Anal 21:4–13. https://doi.org/10.1002/pca.1188
Zhang Q-W, Lin L-G, Ye W-C (2018) Techniques for extraction and isolation of natural products: a comprehensive review. Chin Med 13:20. https://doi.org/10.1186/s13020-018-0177-x
Camel V (2014) Extraction methodologies in plants: general introduction. In: Encyclopedia of analytical chemistry. Elsevier, Amsterdam, pp 1–26
Fiehn O, Wohlgemuth G, Scholz M et al (2008) Quality control for plant metabolomics: reporting MSI-compliant studies. Plant J 53:691–704. https://doi.org/10.1111/j.1365-313X.2007.03387.x
Sumner LW, Amberg A, Barrett D et al (2007) Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics 3:211–221. https://doi.org/10.1007/s11306-007-0082-2
Goodacre R, Broadhurst D, Smilde AK et al (2007) Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3:231–241. https://doi.org/10.1007/s11306-007-0081-3
Fiehn O, Sumner LW, Rhee SY et al (2007) Minimum reporting standards for plant biology context information in metabolomic studies. Metabolomics 3:195–201. https://doi.org/10.1007/s11306-007-0068-0
Vinay CM, Udayamanoharan SK, Prabhu Basrur N et al (2021) Current analytical technologies and bioinformatic resources for plant metabolomics data. Plant Biotechnol Rep 15:561–572. https://doi.org/10.1007/s11816-021-00703-3
Robards K, Ryan D (2022) High performance liquid chromatography: separations. In: Robards K, Ryan D (eds) Principles and practice of modern chromatographic methods, 2nd edn. Academic Press, Amsterdam, pp 283–336
Tolstikov VV, Fiehn O (2002) Analysis of highly polar compounds of plant origin: combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry. Anal Biochem 301:298–307. https://doi.org/10.1006/abio.2001.5513
Antonio C, Larson T, Gilday A et al (2008) Hydrophilic interaction chromatography/electrospray mass spectrometry analysis of carbohydrate-related metabolites from Arabidopsis thaliana leaf tissue. Rapid Commun Mass Spectrom 22:1399–1407. https://doi.org/10.1002/rcm.3519
Dührkop K, Fleischauer M, Ludwig M et al (2019) SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat Methods 16:299–302. https://doi.org/10.1038/s41592-019-0344-8
da Silva RR, Dorrestein PC, Quinn RA (2015) Illuminating the dark matter in metabolomics. Proc Natl Acad Sci U S A 112:12549–12550
Bittremieux W, Wang M, Dorrestein PC (2022) The critical role that spectral libraries play in capturing the metabolomics community knowledge. Metabolomics 18:94. https://doi.org/10.1007/s11306-022-01947-y
Wang M, Carver JJ, Phelan VV et al (2016) Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat Biotechnol 34:828–837. https://doi.org/10.1038/nbt.3597
Quinn RA, Nothias L-F, Vining O et al (2017) Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trends Pharmacol Sci 38:143–154. https://doi.org/10.1016/j.tips.2016.10.011
van der Hooft JJJ, Wandy J, Barrett MP et al (2016) Topic modeling for untargeted substructure exploration in metabolomics. Proc Natl Acad Sci U S A 113:13738–13743. https://doi.org/10.1073/pnas.1608041113
de Jonge NF, Louwen JJR, Chekmeneva E et al (2023) MS2Query: reliable and scalable MS2 mass spectra-based analogue search. Nat Commun 14:1752. https://doi.org/10.1038/s41467-023-37446-4
Dührkop K, Shen H, Meusel M et al (2015) Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc Natl Acad Sci U S A 112:12580–12585. https://doi.org/10.1073/pnas.1509788112
Dührkop K, Nothias L-F, Fleischauer M et al (2021) Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol 39:462–471. https://doi.org/10.1038/s41587-020-0740-8
Ernst M, Nothias L-F, van der Hooft JJJ et al (2019) Assessing specialized metabolite diversity in the cosmopolitan plant genus euphorbia L. Front Plant Sci 10:846. https://doi.org/10.3389/fpls.2019.00846
Silva E, da Graça JP, Porto C et al (2020) Unraveling Asian soybean rust metabolomics using mass spectrometry and molecular networking approach. Sci Rep 10:138. https://doi.org/10.1038/s41598-019-56782-4
Kang KB, Ernst M, van der Hooft JJJ et al (2019) Comprehensive mass spectrometry-guided phenotyping of plant specialized metabolites reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae. Plant J 98:1134–1144. https://doi.org/10.1111/tpj.14292
Chambers MC, Maclean B, Burke R et al (2012) A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 30:918–920. https://doi.org/10.1038/nbt.2377
Schmid R, Heuckeroth S, Korf A et al (2023) Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol 41:447. https://doi.org/10.1038/s41587-023-01690-2
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303
Nothias L-F, Petras D, Schmid R et al (2020) Feature-based molecular networking in the GNPS analysis environment. Nat Methods 17:905–908. https://doi.org/10.1038/s41592-020-0933-6
Allard P-M, Gaudry A, Quirós-Guerrero L-M et al (2022) Open and reusable annotated mass spectrometry dataset of a chemodiverse collection of 1,600 plant extracts. Gigascience 12:giac124. https://doi.org/10.1093/gigascience/giac124
Mutabdžija L, Myoli A, de Jonge NF et al (2023) Ranunculales supplementary data. Zenodo. https://doi.org/10.5281/ZENODO.7784920
Rogers S, Ong CW, Wandy J et al (2019) Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra. Faraday Discuss 218:284–302. https://doi.org/10.1039/c8fd00235e
Ernst M, Kang KB, Caraballo-Rodríguez AM et al (2019) MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools. Meta 9:144. https://doi.org/10.3390/metabo9070144
Hoffmann MA, Nothias L-F, Ludwig M et al (2022) High-confidence structural annotation of metabolites absent from spectral libraries. Nat Biotechnol 40:411–421. https://doi.org/10.1038/s41587-021-01045-9
Wandy J, Zhu Y, van der Hooft JJJ et al (2018) Ms2lda.org: web-based topic modelling for substructure discovery in mass spectrometry. Bioinformatics 34:317–318. https://doi.org/10.1093/bioinformatics/btx582
Guo J, Huan T (2020) Comparison of full-scan, data-dependent, and data-independent acquisition modes in liquid chromatography-mass spectrometry based untargeted metabolomics. Anal Chem 92:8072–8080. https://doi.org/10.1021/acs.analchem.9b05135
Davies V, Wandy J, Weidt S et al (2021) Rapid development of improved data-dependent acquisition strategies. Anal Chem 93:5676–5683. https://doi.org/10.1021/acs.analchem.0c03895
mzML 1.1.0 Specification. https://www.psidev.info/mzML. Accessed 2 Feb 2023
Djoumbou Feunang Y, Eisner R, Knox C et al (2016) ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform 8:61. https://doi.org/10.1186/s13321-016-0174-y
Acknowledgments
T.P. is supported by the Czech Science Foundation (GA CR) grant 21-11563 M and by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 891397. L.M. is co-financed by the Governments of Czechia, Hungary, Poland, and Slovakia through Visegrad Grant 52210524 from the International Visegrad Fund. We would like to thank Fred Rooks for editing this manuscript. Figures were created using https://biorender.com. J.J.J.v.d.H. declares that he is member of the Scientific Advisory Board of NAICONS Srl., Milano, Italy, and consulting for Corteva Agriscience, Indianapolis, IN, USA.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Mutabdžija, L. et al. (2024). Studying Plant Specialized Metabolites Using Computational Metabolomics Strategies. In: Maghuly, F. (eds) Plant Functional Genomics. Methods in Molecular Biology, vol 2788. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3782-1_7
Download citation
DOI: https://doi.org/10.1007/978-1-0716-3782-1_7
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-3781-4
Online ISBN: 978-1-0716-3782-1
eBook Packages: Springer Protocols