Inverstigatoin Of Glucosinolate Metabolism Network By Omics Apporaches

Glucosinolate are group of secondary metabolites containing nitrogen and sulfur. Since its roles in plant-envorinment interactions, a lot of attentions have been paid for its metabolism. Last two decades have seen great progresses in glucosinolate biosynthesis. Nowdays, almost all genes in glucosinolate biosynthesis pathway have been identificated. To undersatand plant molecular connections between glucosinilate metabolism and other metabolism pathways/bioprocesses, we employed bioinformatic tools to analyze molecular network of glucosinolate biosynthesis on one hand. On the other hand, we used metabolimcs and proteimics approaches to inverstigate the metabolome and proteome changes response to perturbation of glucosinolate biosynthesis. The results from two parts show, glucosinolate biosynthesis involves the whole plant metabolism which regulates different metabolism pathways. Glucosinolate metabolism is not only a metabolism pathway which extand from primary metabolism, but is also tightly related to cellular signaling and other metabolism pathways.Bioinformatic tools (AraNet, GeneMania and ATTED-II)with large database and efficient algorithm were used for analyze the metabolism network related to glucosinolate biosynthesis. Our results show that the metabolism network includes stress and defence, secondary metabolism, amino acid metabolism, transport, hormone metabolism, protein metabolism, nucleotide metabolism, carbohydrate metabolism, signal transduction, translation, cell well, development and lipid metabolism. In order to validate the network, on one hand, microarray data of candidate mutants were used for profile glucosinolate biosynthesis genes expression. On the other hand, candidate genemutant and overexpression plants were used for futher glucosinolate analysis. Validation results showed,using bioinformatic tools for analysis glucosinolate metabolism network is a powerful way to discovery of new candidate genes and connections between glucosinolate pathway and other pathways. Creation of an in silico network of glucosinolate biosynthesis will allow the generation of many testable hypotheses and ultimately enable predicative biology.Meanwhile, we employed nontargeted metabolite analysis performed by ultrahigh-performance liquid chromatography/tandem mass spectrometry and gas chromatography/mass spectrometry to inverstigate metabolome responsed to glucosinolate perturbation.We were able to identify73metabolites display significant changes in levels, which are clustered into eight functional groups, i.e. amino acids, carbohydrate, lipid, cofactors, nucleotide, peptide, hormone, and secondary metabolites. Two complementary proteomic approaches2D-DIGE and iTRAQ were employed to investigate global protein changes in response to glucosinolate perturbation. Both2D-DIGE and iTRAQ identified215differentially expressed proteins, which were overrepresented into eight groups, i.e. metabolism, protein binding, enery, defense, protein fate, transport, interaction with the environment, and protein synthesis.With findings from bioinformatic, metabolomic, and proteomics analysis, we draw conclusions:1. amino acids and chloroplast metabolism are higly correlated to glucosinolate metabolism;2. Cross-talk between glucosinolate biosynthesis and hormone metabolism;3. glucosinolate perturbation causes oxidatitve stress;4. glucosinolate metabolism realated to plant transport system.