Arabidopsis, maize, soybean, Brassica rapa
Robust methods that connect observable macroscopic or physiological phenotypes of plants with their genetic determinants are badly needed. 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 analysis (MFA) provides a direct route for both the discovery (and subsequent manipulation) of the molecular bases of any number of complex plant behaviors. While MFA has been used to great effect in microbial systems, it has been less widely employed in plants.
The applicability of dynamic metabolic flux analysis to intact plants will be improved by:
1) Optimization of methods for measurement of dynamic metabolic fluxes in intact plant systems. The approach uses timed stable isotope labeled nutrient incorporation, mass spectral analysis, and automated data extraction and calculation of dynamic fluxes.
2) Development of a procedure for finding additional connectivity and pathway components of metabolic network models using dynamic flux information. The method will be tested using a publically available metabolic network model for Arabidopsis with flux data collected from Arabidopsis plants subjected to multiple stress conditions.
3) Development of microsampling approaches, including single cell sampling, for dynamic flux analysis by examining crowding stress in maize. Crowding stress is a major limitation of yield for many crops and involves a number of physiological signaling inputs, making it an ideal test for the application of these approaches. Differential dynamic flux analysis will initially be applied to sectioned plant materials and subsequently to microsampling techniques.
Most current applications of MFA used for studies in plants require that metabolic processes be in a steady state. This requirement has significantly limited the range of application of MFA to address important questions in plant biology and agriculture. We plan to further develop dynamic-MFA into an approach that provides valuable genome-scale metabolic flux information for intact plants. The approach will involve growing plants with timed exposure to stable isotope labeled nutrients followed by harvest, extraction, LC- and GC-MS analysis, automated data extraction and calculation of metabolite turnover. Absolute quantities of metabolites in unlabeled (natural abundance) time points will be measured by Multiple Reaction Monitoring (MRM) using LC- or GC-triple quadrupole MS and a labeled standard mixture by isotope dilution. Metabolite fluxes will be calculated using these quantities and the turnover data.
We plan to use dynamic metabolic fluxes measured using these procedures as constraints for dynamic MFA using a publically available Arabidopsis genome-scale metabolic network model. We will then experimentally test the model empirically in several ways including using measurements taken from plants grown under a series of stress conditions. We hypothesize that growth under stress conditions will result in major changes in metabolic flux that will allow us to test the model more completely for errors and inconsistencies.
We will also develop an optimized set of microsampling approaches for MFA and metabolomics experimentation. To do this we will explore the utility of various approaches for sampling a small number of cells down to a single cell from different plant tissue. General approaches include laser microdissection and direct sampling from plant tissues using microcapillaries that are subsequently coupled directly to nanoelectrospray ionization (nanoESI) MS-based metabolite analysis. Optimized microsampling approaches will be used in conjunction with more typical sampling techniques for dynamic MFA of maize seedling subjected to different ground reflected far-red/red light ratios to either mimic the conditions that elicit a crowding response or not.