title: Metabolic flux analysis using ¹³C peptide label measurements.
abstract: ¹³C metabolic flux analysis (MFA) has become the experimental method of choice to investigate the cellular metabolism of microbes, cell cultures and plant seeds. Conventional steady-state MFA utilizes isotopic labeling measurements of amino acids obtained from protein hydrolysates. To retain spatial information in conventional steady-state MFA, tissues or subcellular fractions must be dissected or biochemically purified. In contrast, peptides retain their identity in complex protein extracts, and may therefore be associated with a specific time of expression, tissue type and subcellular compartment. To enable 'single-sample' spatially and temporally resolved steady-state flux analysis, we investigated the suitability of 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 of a peptide. We investigated the requirements for the unique deconvolution of PMDs into amino acid mass distributions (AAMDs), the influence of peptide sequence length on parameter sensitivity, and how AAMD and flux estimates that are determined through deconvolution compare to estimates from a conventional GC-MS measurement-based approach. Deconvolution of PMDs of the storage protein β-conglycinin of soybean (Glycine max) resulted in good AAMD and flux estimates if fluxes were directly fitted to PMDs. Unconstrained deconvolution resulted in inferior AAMD and flux estimates. PMD measurements do not include amino acid backbone fragments, which increase the information content in GC-MS-derived analyses. Nonetheless, the resulting flux maps were of comparable quality due to the precision of Orbitrap quantification and the larger number of peptide measurements.
authors.affiliation: Department of Plant Biology, University of Minnesota, 1500 Gortner Avenue, St Paul, MN, 55108, USA.
authors.forename: Dominic E
authors.initials: DE
authors.lastname: Mandy
authors.forename: Joshua E
authors.initials: JE
authors.lastname: Goldford
authors.forename: Hong
authors.initials: H
authors.lastname: Yang
authors.forename: Doug K
authors.initials: DK
authors.lastname: Allen
authors.forename: Igor G L
authors.initials: IG
authors.lastname: Libourel
mesh_terms: D052179:Antigens, Plant / Q000032:analysis* / Q000378:metabolism; D002247:Carbon Isotopes / Q000032:analysis; D002853:Chromatography, Liquid; D008401:Gas Chromatography-Mass Spectrometry; D005916:Globulins / Q000032:analysis* / Q000378:metabolism; D064688:Metabolic Flux Analysis / Q000379:methods*; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D010455:Peptides / Q000032:analysis* / Q000378:metabolism; D040901:Proteomics; D055314:Seed Storage Proteins / Q000032:analysis* / Q000378:metabolism; D012680:Sensitivity and Specificity; D030262:Soybean Proteins / Q000032:analysis* / Q000378:metabolism; D013025:Glycine max / Q000378:metabolism*
chemical_list: D052179:Antigens, Plant; D002247:Carbon Isotopes; D005916:Globulins; D010455:Peptides; D055314:Seed Storage Proteins; D030262:Soybean Proteins; C013894:beta-conglycinin protein, Glycine max
keywords: isotopic labeling; metabolic flux analysis; orbital trap; primary metabolism; proteomics; subcellular compartmentation; technical advance