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United States Department of Agriculture

Agricultural Research Service

Research Project: GENETIC ENHANCEMENT OF SOYBEAN SEED VALUE BY BIOTECHNOLOGY Title: Metabolic flux analysis using 13C peptide label measurements

Authors
item Mandy, Dominic -
item Goldford, Joshua -
item Yang, Hong -
item Allen, Douglas
item Libourel, Igor -

Submitted to: Plant Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 15, 2013
Publication Date: November 26, 2013
Repository URL: http://handle.nal.usda.gov/10113/58573
Citation: Mandy, D., Goldford, J., Yang, H., Allen, D.K., Libourel, I. 2013. Metabolic flux analysis using 13C peptide label measurements. Plant Journal. 77:476-486.

Interpretive Summary: Plants are multicellular (i.e. they contain different types of cells) and they contain compartments within cells that separate metabolic processes. This heterogeneity within and across cells make it difficult to experimentally analyze the metabolic events that describe plant function. As a result it remains challenging to modify seed composition, in important crops including soybean, because our experiments do not adequately describe this spatial complexity. Isotopes (e.g. 13-carbon) can be provided to growing plant tissues and used to document their metabolism by experimentally tracking the movement of the isotopes. We developed new methods to computationally determine the labeling in amino acids from measured labeling in peptides. Peptides are synthesized with amino acids from different locations; therefore they can provide some of the needed compartmental information. Soybean protein was isotopically labeled (13C-glucose and 13C-glutamine), then processed into peptides and the isotope incorporation was measured with mass spectrometry. Methods to mathematically establish the labeling in amino acids from measured peptides were developed. Our results were applied to a metabolic network description for soybeans and used to establish the fluxes (flows of metabolites through different metabolic pathways). The strengths and weaknesses of the approach were compared to traditional methods for metabolic flux analysis. The approach is important because it can provide insights to compartmentation in cells as well as metabolic events that occur at different times. Therefore these methods will be used to extend metabolic engineering and produce soybeans with altered seed composition.

Technical Abstract: 13C metabolic flux analysis (MFA) has become the experimental method of choice to investigate cellular metabolism. MFA has established flux maps of central metabolism for dozens of microbes, cell cultures, and plant seeds. Steady-state MFA utilizes isotopic labeling measurements of amino acids obtained from protein hydrolysates. Whole cell– or tissue–hydrolysates contain no spatial or temporal information. As a result, flux maps that are tissue specific, or occur only during a short developmental stage such as a phase of the cell cycle, have not yet been reported. To unlock the potential for spatial and temporal flux analysis, we investigated peptide mass distributions (PMDs) as an alternative to amino acid label measurements. PMDs are the discrete convolution of the mass distributions of the constituent amino acids. In principle, amino acid mass distributions (AAMDs) should be obtainable through deconvolution, given a sufficient number of PMDs. This work investigated the requirements for the unique deconvolution of PMDs into AAMDs, the influence of peptide sequence length on parameter sensitivity, and how AAMD and flux estimates determined through deconvolution compared to a conventional GC-MS measurement-based approach. Deconvolution of PMDs of the soy storage protein ß-conglycinin only resulted in good AAMD and flux estimates, if fluxes were directly fitted to PMDs. Unconstrained deconvolution resulted in inferior AAMD estimates. PMDs did not include amino acid backbone fragmentation that increases the information content in GC-MS-derived analyses, nonetheless, the resulting flux maps were of comparable quality and enable spatially and temporally resolved flux analysis.

Last Modified: 12/18/2014
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