Improving Dynamic Metabolic Flux Analysis for the Discovery of Molecular Determinants of Plant Phenotypes

PI: Adrian D. Hegeman (University of Minnesota - Twin Cities)

CoPI: Jerry D. Cohen (University of Minnesota - Twin Cities)

Key Collaborator: Aaron Rendahl (University of Minnesota - Twin Cities)

With the recent rapid expansion in accessible genome and mRNA nucleotide sequence data there is an increasing need for robust methods for connecting observable plant phenotypes with changes at the molecular level. Measurement of metabolic flux (the flow of matter through an organism's network of metabolic pathways) is a direct molecular indicator of an organism's phenotype. Metabolic flux information describes how nutrient resources and stores are used by which segments of a metabolic network to generate and accumulate products required for growth, development, and differentiation, integrated with responses to environmental conditions. Correspondingly, metabolic flux analysis (MFA) provides a direct route for both the discovery (and subsequent manipulation) of the molecular basis of any number of complex plant behaviors. Unfortunately, most MFA is currently performed using cultured cells, both to provide homogeneity and as an easy method for introduction of isotope labeled nutrients. Several challenges need to be addressed before dynamic-MFA can be applied practically to intact plants for molecular phenotyping. This project will provide for 1) optimize methods for measurement of dynamic metabolic fluxes in intact plant systems using timed stable isotope labeled nutrient incorporation, mass spectral analysis, and automated data extraction and calculation of dynamic fluxes; 2) develop a procedure for finding additional connectivity and pathway components of metabolic network models using dynamic flux information using a publicly available metabolic network model for Arabidopsis with flux data collected from Arabidopsis plants subjected to multiple stress conditions and 3) develop microsampling approaches for dynamic flux analysis by examining crowding stress in maize. Computational and bioinformatics resources for metabolic flux analysis workflows will be created and distributed.

In this project, postdoctoral scientists and graduate students will be exposed to a unique multidisciplinary approach to metabolomics. In addition, the project will continue to offer a summer workshop for graduate students, postdoctoral scientists and others interested in stable isotope approaches for understanding metabolic flux measurement and how it might be used for biological research. Short-term dissemination of data, processing algorithms and methods will be accomplished by means of the project website (http://plantmetabolomics.cfans.umn.edu/).