Maize specialized metabolome networks reveal organ-preferential mixed glycosides
- Author
- Sandrien Desmet (UGent) , Yvan Saeys (UGent) , Kevin Verstaen (UGent) , Rebecca Dauwe (UGent) , Hoon Kim, Claudiu Niculaes (UGent) , Atsushi Fukushima, Geert Goeminne (UGent) , Ruben Vanholme (UGent) , John Ralph, Wout Boerjan (UGent) and Kris Morreel (UGent)
- Organization
- Abstract
- Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/).
- Keywords
- Mass spectrometry, Specialized metabolism, Spectral metadata analysis, Zea mays, MASS-SPECTROMETRY, PHENYLACETIC ACID, AUXIN BIOSYNTHESIS, ARABIDOPSIS, FRAGMENTATION, IDENTIFICATION, ELUCIDATION, ANNOTATION, PREDICTION, DIVERSITY
Downloads
-
Desmet et al. 2021 Computational and Structural Biotechnology Journal 19 1127.pdf
- full text (Published version)
- |
- open access
- |
- |
- 3.33 MB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8693206
- MLA
- Desmet, Sandrien, et al. “Maize Specialized Metabolome Networks Reveal Organ-Preferential Mixed Glycosides.” COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, vol. 19, 2021, pp. 1127–44, doi:10.1016/j.csbj.2021.01.004.
- APA
- Desmet, S., Saeys, Y., Verstaen, K., Dauwe, R., Kim, H., Niculaes, C., … Morreel, K. (2021). Maize specialized metabolome networks reveal organ-preferential mixed glycosides. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 19, 1127–1144. https://doi.org/10.1016/j.csbj.2021.01.004
- Chicago author-date
- Desmet, Sandrien, Yvan Saeys, Kevin Verstaen, Rebecca Dauwe, Hoon Kim, Claudiu Niculaes, Atsushi Fukushima, et al. 2021. “Maize Specialized Metabolome Networks Reveal Organ-Preferential Mixed Glycosides.” COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 19: 1127–44. https://doi.org/10.1016/j.csbj.2021.01.004.
- Chicago author-date (all authors)
- Desmet, Sandrien, Yvan Saeys, Kevin Verstaen, Rebecca Dauwe, Hoon Kim, Claudiu Niculaes, Atsushi Fukushima, Geert Goeminne, Ruben Vanholme, John Ralph, Wout Boerjan, and Kris Morreel. 2021. “Maize Specialized Metabolome Networks Reveal Organ-Preferential Mixed Glycosides.” COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 19: 1127–1144. doi:10.1016/j.csbj.2021.01.004.
- Vancouver
- 1.Desmet S, Saeys Y, Verstaen K, Dauwe R, Kim H, Niculaes C, et al. Maize specialized metabolome networks reveal organ-preferential mixed glycosides. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL. 2021;19:1127–44.
- IEEE
- [1]S. Desmet et al., “Maize specialized metabolome networks reveal organ-preferential mixed glycosides,” COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, vol. 19, pp. 1127–1144, 2021.
@article{8693206, abstract = {{Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/).}}, author = {{Desmet, Sandrien and Saeys, Yvan and Verstaen, Kevin and Dauwe, Rebecca and Kim, Hoon and Niculaes, Claudiu and Fukushima, Atsushi and Goeminne, Geert and Vanholme, Ruben and Ralph, John and Boerjan, Wout and Morreel, Kris}}, issn = {{2001-0370}}, journal = {{COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL}}, keywords = {{Mass spectrometry,Specialized metabolism,Spectral metadata analysis,Zea mays,MASS-SPECTROMETRY,PHENYLACETIC ACID,AUXIN BIOSYNTHESIS,ARABIDOPSIS,FRAGMENTATION,IDENTIFICATION,ELUCIDATION,ANNOTATION,PREDICTION,DIVERSITY}}, language = {{eng}}, pages = {{1127--1144}}, title = {{Maize specialized metabolome networks reveal organ-preferential mixed glycosides}}, url = {{http://doi.org/10.1016/j.csbj.2021.01.004}}, volume = {{19}}, year = {{2021}}, }
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: