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Abstract
BackgroundTo elucidate the interaction of dynamics among modules that constitute biological systems, comprehensive datasets obtained from "omics" technologies have been used. In recent plant metabolomics approaches, the reconstruction of metabolic correlation networks has been attempted using statistical techniques. However, the results were unsatisfactory and effective data-mining techniques that apply appropriate comprehensive datasets are needed.ResultsUsing capillary electrophoresis mass spectrometry (CE-MS) and capillary electrophoresis diode-array detection (CE-DAD), we analyzed the dynamic changes in the level of 56 basic metabolites in plant foliage (Oryza sativa L. ssp. japonica) at hourly intervals over a 24-hr period. Unsupervised clustering of comprehensive metabolic profiles using Kohonen's self-organizing map (SOM) allowed classification of the biochemical pathways activated by the light and dark cycle. The carbon and nitrogen (C/N) metabolism in both periods was also visualized as a phenotypic linkage map that connects network modules on the basis of traditional metabolic pathways rather than pairwise correlations among metabolites. The regulatory networks of C/N assimilation/dissimilation at each time point were consistent with previous works on plant metabolism. In response to environmental stress, glutathione and spermidine fluctuated synchronously with their regulatory targets. Adenine nucleosides and nicotinamide coenzymes were regulated by phosphorylation and dephosphorylation. We also demonstrated that SOM analysis was applicable to the estimation of unidentifiable metabolites in metabolome analysis. Hierarchical clustering of a correlation coefficient matrix could help identify the bottleneck enzymes that regulate metabolic networks.ConclusionOur results showed that our SOM analysis with appropriate metabolic time-courses effectively revealed the synchronous dynamics among metabolic modules and elucidated the underlying biochemical functions. The application of discrimination of unidentified metabolites and the identification of bottleneck enzymatic steps even to non-targeted comprehensive analysis promise to facilitate an understanding of large-scale interactions among components in biological systems.
Highlights
To elucidate the interaction of dynamics among modules that constitute biological systems, comprehensive datasets obtained from "omics" technologies have been used
Our target and experimental conditions were strategically determined: 1) we focused on primary metabolic pathways consisting of carbon fixation/respiration- and nitrogen assimilation/dissimilation pathways, and comprehensively quantified related metabolites, 2) the photocycle was the sole environmental factor, and 3) measurements were made at 1-hr intervals to allow the observation of dynamic profiles
High-throughput analysis was conducted with the capillary electrophoresis – mass spectrometry (CE-MS) technology we developed earlier [12,13,14], and has been applied to metabolic profiling in Bacillus subtilis extracts [15] and monitoring of genetic and environmental perturbations in Escherichia coli cells [16]
Summary
To elucidate the interaction of dynamics among modules that constitute biological systems, comprehensive datasets obtained from "omics" technologies have been used. In recent plant metabolomics approaches, the reconstruction of metabolic correlation networks has been attempted using statistical techniques. In the post-genome era, comprehensive data from "omics" technologies (genomics, transcriptomics, proteomics, and metabolomics) have been extensively analyzed to elucidate the underlying biochemical networks that elaborately regulate cellular mechanisms. As statistical inference requires large amounts of data measured under similar conditions in transcriptomics [11], the verification of network dynamics for known pathways must precede attempts to identify unknown network structures. It appears that each metabolic profile is measured under method-specific, presumably biased conditions
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