Abstract
During the last decade genome sequencing has experienced a rapid technological development resulting in numerous sequencing projects and applications in life science. In plant molecular biology, the availability of sequence data on whole genomes has enabled the reconstruction of metabolic networks. Enzymatic reactions are predicted by the sequence information. Pathways arise due to the participation of chemical compounds as substrates and products in these reactions. Although several of these comprehensive networks have been reconstructed for the genetic model plant Arabidopsis thaliana, the integration of experimental data is still challenging. Particularly the analysis of subcellular organization of plant cells limits the understanding of regulatory instances in these metabolic networks in vivo. In this study, we develop an approach for the functional integration of experimental high-throughput data into such large-scale networks. We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions. We demonstrate how to link the metabolite covariance matrix of different Arabidopsis thaliana accessions with the subcellular metabolic network model for the inverse calculation of the biochemical Jacobian, finally resulting in the calculation of a matrix which satisfies a Lyaponov equation. In this way, different strategies of metabolite compartmentation and involved reactions were identified in the accessions when exposed to low temperature.
Highlights
The rapidly increasing knowledge about whole plant genome sequences represents a corner stone in the understanding of plant metabolism
We present a subcellular metabolic network model comprising 524 metabolic intermediates and 548 metabolic interactions derived from a total of 2769 reactions
We reduced the compartments in the model to the cytoplasm, plastid and vacuole which can experimentally be analyzed from the same sample using the non-aqueous fractionation (NAF) method (Nägele and Heyer, 2013)
Summary
The rapidly increasing knowledge about whole plant genome sequences represents a corner stone in the understanding of plant metabolism. Due to the huge metabolic coverage of GEMs, which, in principle, comprises all metabolic interactions known so far from genome annotation, strategies for genome-scale experiments are needed for efficient validation of model outputs In this context, flux measurements and high-throughput measurements of the transcriptome, proteome and metabolome play a crucial role. We connected data from metabolomics experiments to a simplified metabolic network structure of leaf primary metabolism in Arabidopsis thaliana to characterize metabolic shifts during cold exposure (Doerfler et al, 2013) This allowed us to differentiate short and long term metabolic response to low temperature and to identify key points of regulation such as the interface of primary and secondary metabolism mediated by the shikimic acid pathway. It is not surprising that physiological responses to a changing environment could be related to subcellular metabolic reprogramming www.frontiersin.org
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