Present address: Kazusa DNA Research Institute, Kazusa-Kamatari 2-6-7, Kisarazu 292-0818, Japan.
Metabolite annotations based on the integration of mass spectral information
Article first published online: 7 FEB 2008
© 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd
The Plant Journal
Volume 54, Issue 5, pages 949–962, June 2008
How to Cite
Iijima, Y., Nakamura, Y., Ogata, Y., Tanaka, K., Sakurai, N., Suda, K., Suzuki, T., Suzuki, H., Okazaki, K., Kitayama, M., Kanaya, S., Aoki, K. and Shibata, D. (2008), Metabolite annotations based on the integration of mass spectral information. The Plant Journal, 54: 949–962. doi: 10.1111/j.1365-313X.2008.03434.x
Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
- Issue published online: 7 FEB 2008
- Article first published online: 7 FEB 2008
- Received 20 November 2007; revised 21 January 2008; accepted 23 January 2008.
- metabolite annotations;
- secondary metabolites;
- Solanum lycopersicum;
A large number of metabolites are found in each plant, most of which have not yet been identified. Development of a methodology is required to deal systematically with unknown metabolites, and to elucidate their biological roles in an integrated ‘omics’ framework. Here we report the development of a ‘metabolite annotation’ procedure. The metabolite annotation is a process by which structures and functions are inferred for metabolites. Tomato (Solanum lycopersicum cv. Micro-Tom) was used as a model for this study using LC-FTICR-MS. Collected mass spectral features, together with predicted molecular formulae and putative structures, were provided as metabolite annotations for 869 metabolites. Comparison with public databases suggests that 494 metabolites are novel. A grading system was introduced to describe the evidence supporting the annotations. Based on the comprehensive characterization of tomato fruit metabolites, we identified chemical building blocks that are frequently found in tomato fruit tissues, and predicted novel metabolic pathways for flavonoids and glycoalkaloids. These results demonstrate that metabolite annotation facilitates the systematic analysis of unknown metabolites and biological interpretation of their relationships, which provide a basis for integrating metabolite information into the system-level study of plant biology.