Detailed understanding of biochemical pathways and common regulatory elements is a pre-requisite for creating system wide in-silico models and metabolic engineering of biological systems. Such information for complex eukaryotic and novel prokaryotic systems, is traditionally obtained by (a) post gene-sequencing homology based predictions (b) rigorous gene-by-gene or pathway-by-pathway studies. While the former approach is unable to identify presence of novel pathways and regulatory elements specific to the system being studied, the later approach is not high-throughput. A high-throughput experimental design methodology for identifying novel biochemical networks and regulatory elements would be thus, a very important tool for engineering complex eukaryotic and non- “model prokaryotic” systems: such as mammalian system, new species of filamentous fungi, and not-fully-sequenced but commercially important plants such as switch grass, woody plants, vegetable plants, cotton, etc.

Metabolomics – the newest of the “-omic” technologies, provides a high-throughput, organism independent, tool for obtaining the metabolomic fingerprint of the biological system being studied. In order to use metabolomic analysis for identifying novel biochemical pathways and regulatory elements in whole plant systems we obtained time-series (reference + eight time points in each series) metabolomic profiles of response of 12 day old Arabidopsis thaliana seedlings grown in liquid cultures under normal (control) and seven perturbed conditions. The perturbed conditions were obtained by increasing CO₂ levels, salt stress, sugar (trehalose) signal and hormone (ethylene) signal applied individually and the last three in combination with the first. The polar metabolomic profiles were obtained using Gas Chromatography-Mass Spectrometry (GC-MS) analysis. The obtained raw GC-MS metabolomic profiles were corrected for various derivatization biases using a novel metabolomic data correction algorithm [1] which increased accuracy of metabolomic analysis by an order of magnitude.

Each of these perturbations altered metabolite concentrations significantly, with some metabolites showing up to 30 fold increase / decrease in their relative concentration at individual time points in response to the perturbations. Comparison of response of metabolites belonging to a known metabolomic pathway identified novel regulatory elements of important primary metabolism pathways such as Tri Carboxylic Acid (TCA) cycle and Secondary Amino Acid biosynthesis. In addition by measuring correlations between the time profiles of metabolites under eight different conditions, with successive degree of stringency, we were able to reconstruct parts of known biochemical networks and also identify presence of novel pathways and regulatory elements for A. thaliana. The results thus obtained provide important regulatory insights for plants with important commercial metabolic engineering applications. In addition to providing comprehensive information about the biological system being studied and effects of combination of stress, the present study provides a comprehensive methodology for conducting similar metabolomic studies in other complex eukaryotic or novel prokaryotic systems; getting one step closer to high-throughput system wide reconstruction of their unknown biochemical networks.

[1] Kanani H and Klapa MI. “Data Correction Strategy for Metabolomics Analysis using Gas Chromatography-Mass Spectrometry”, Metabolic Engineering, 2007, 9(1):35-51.

Author Present address:

Dr. Harin H. Kanani: Pioneer Hi-Bred International - A DuPont Company, Johnston, Iowa – 50131, USA.

Dr. Maria I. Klapa: Institute of Chemical Engineering and High Temperature Chemical Processes, Foundation of Research and Technology, Hellas (FORTH/ICE-HT), Patras, Greece.

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