Arbeitsgruppe Metabolische Diversität
Leitung: Dr. John Charles D'Auria
Metabolic diversity encompasses a wide array of biochemical pathways and processes. The implementation of the methods and experiments used by the D’Auria group will result in a variety of data types. The expected outcomes will range from quantified, targeted metabolites for specific comparisons of primary essential biomolecules to non-targeted screens to identify differences among populations of differentially treated plants or allelic differences within a population of plants. The integrative analyses of primary and secondary metabolic status, integrated with enzyme activity measurements will provide a deep insight into the phenotypic performance parameters (growth rates, vitality, seed viability and yield) necessary to provide the further development of hypotheses related to understanding the interplay of genetics and metabolism in cultivated plants. In addition, the identification of metabolic regulators, both advantageous (positive) and disadvantageous (negative) to the full complement of metabolites being produced under particular environmental stimuli or genetic background with further aid in the ultimate goal of optimizing crop vitality and productivity. These regulators will be fully characterized at the molecular and biochemical level in order to gain mechanistic understanding of their production, storage, and modulation of existing pathways. Furthermore, the D’Auria lab aims to make a metabolism-related specific contribution to the assignment of gene functions in the form of annotation of enzyme class and activity of the high proportion of genes identified as ‘putative’ or more importantly, ‘unknown function’ that are pervasive in complex cultivated crop genomes. In addition to identifying structural genes encoding biosynthetic enzymes, the group will also assist in elucidation and annotation of regulatory proteins. This line of inquiry and experimentation will proceed via the existing and expanding capabilities of the group to perform high-throughput metabolic profile analyses.