The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
Metabolomic technologies and their application to the study of plants and plant–host interactions
Abstract
Metabolomics is perhaps the ultimate level of post‐genomic analysis as it can reveal changes in metabolite fluxes that are controlled by only minor changes within gene expression measured using transcriptomics and/or by analysing the proteome that elucidates post‐translational control over enzyme activity. Metabolic change is a major feature of plant genetic modification and plant interactions with pathogens, pests, and their environment. In the assessment of genetically modified plant tissues, metabolomics has been used extensively to explore by‐products resulting from transgene expression and scenarios of substantial equivalence. Many studies have concentrated on the physiological development of plant tissues as well as on the stress responses involved in heat shock or treatment with stress‐eliciting molecules such as methyl jasmonic acid, yeast elicitor or bacterial lipopolysaccharide. Plant–host interactions represent one of the most biochemically complex and challenging scenarios that are currently being assessed by metabolomic approaches. For example, the mixtures of pathogen‐colonised and non‐challenged plant cells represent an extremely heterogeneous and biochemically rich sample; there is also the further complication of identifying which metabolites are derived from the plant host and which are from the interacting pathogen. This review will present an overview of the analytical instrumentation currently applied to plant metabolomic analysis, literature within the field will be reviewed paying particular regard to studies based on plant–host interactions and finally the future prospects on the metabolomic analysis of plants and plant–host interactions will be discussed.
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Goodall, C. (1988) · Technometrics · doi:10.1080/00401706.1988.10488412 · Cited by 8,675
Jolliffe, I. T. (1986) · Springer Series in Statistics · doi:10.1007/978-1-4757-1904-8 · Cited by 8,273
Correlation between Protein and mRNA Abundance in Yeast
Gygi, S. P., Rochon, Y., Franza, B. R., & Aebersold, R. (1999) · Molecular and Cellular Biology · doi:10.1128/mcb.19.3.1720 · Cited by 3,819
Increase in signal-to-noise ratio of > 10,000 times in liquid-state NMR
Ardenkjær-Larsen, J. H., Fridlund, B., Gram, A., Hansson, G., Hansson, L., Lerche, M. H., Servin, R., Thaning, M., & Golman, K. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1733835100 · Cited by 2,861
Multivariate Statistical Methods: A Primer.
RAJ, & Manly, B. F. J. (1996) · Journal of the American Statistical Association · doi:10.2307/2291702 · Cited by 2,451
Metabolite profiling for plant functional genomics
Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000) · Nature Biotechnology · doi:10.1038/81137 · Cited by 2,127
GMD@CSB.DB: the Golm Metabolome Database
Kopka, J., Schauer, N., Krueger, S., Birkemeyer, C., Usadel, B., Bergmuller, E., Dormann, P., Weckwerth, W., Gibon, Y., Stitt, M., et al. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti236 · Cited by 1,382
The Orbitrap: a new mass spectrometer
Hu, Q., Noll, R. J., Li, H., Makarov, A., Hardman, M., & Graham Cooks, R. (2005) · Journal of Mass Spectrometry · doi:10.1002/jms.856 · Cited by 1,333
Metabolomics by numbers: acquiring and understanding global metabolite data
Goodacre, R., Vaidyanathan, S., Dunn, W. B., Harrigan, G. G., & Kell, D. B. (2004) · Trends in Biotechnology · doi:10.1016/j.tibtech.2004.03.007 · Cited by 1,249
Self-citationMetabolomics: Current analytical platforms and methodologies
(2005) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2004.11.021 · Cited by 1,192
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Current approaches and challenges for the metabolite profiling of complex natural extracts
Wolfender, J.-L., Marti, G., Thomas, A., & Bertrand, S. (2015) · Journal of Chromatography A · doi:10.1016/j.chroma.2014.10.091 · Cited by 635
Review of recent developments in GC–MS approaches to metabolomics-based research
Beale, D. J., Pinu, F. R., Kouremenos, K. A., Poojary, M. M., Narayana, V. K., Boughton, B. A., Kanojia, K., Dayalan, S., Jones, O. A. H., & Dias, D. A. (2018) · Metabolomics · doi:10.1007/s11306-018-1449-2 · Cited by 531
Fusarium-Produced Mycotoxins in Plant-Pathogen Interactions
Perincherry, L., Lalak-Kańczugowska, J., & Stępień, Ł. (2019) · Toxins · doi:10.3390/toxins11110664 · Cited by 298
Designing salt stress‐resilient crops: Current progress and future challenges
Liang, X., Li, J., Yang, Y., Jiang, C., & Guo, Y. (2024) · Journal of Integrative Plant Biology · doi:10.1111/jipb.13599 · Cited by 292
Toxicology and risk assessment of coumarin: Focus on human data
Abraham, K., Wöhrlin, F., Lindtner, O., Heinemeyer, G., & Lampen, A. (2010) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.200900281 · Cited by 292
Metadata
- Authors
- Allwood, J. W., Ellis, D. I., & Goodacre, R.
- Venue
- Physiologia Plantarum
- Publisher
- Wiley
- Published
- 2007-11-22
- Volume
- 132
- Issue
- 2
- Pages
- 117-135
- ISSN
- 0031-9317 (ISSN-L)
- 1399-3054
- OpenAlex
- W2109150844
- Cited by
- 301
- References
- 108
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- closed
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