Analysis of isotopic labeling in peptide fragments by tandem mass spectrometry

Authors: Allen, Douglas - Doug; LIBOUREL, IGOR - University Of Minnesota

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/13/2014
Publication Date: 3/13/2014
Publication URL: http://handle.nal.usda.gov/10113/58740
Citation: Allen, D.K., Libourel, I.G. 2014. Analysis of isotopic labeling in peptide fragments by tandem mass spectrometry. PLoS One. 9(3):e91537. DOI:10.1371/journal.pone.0091537.

Interpretive Summary: In order to modify soybean seed composition it is critical for us to understand how and where in the cells of the seeds proteins are made, modified, and degraded: information that is currently limited for soybeans. This work describes the use of a hybrid tandem mass spectrometer to measure the isotopic labeling (e.g., 13-carbon and 12-carbon) in peptides obtained from soybean storage proteins. Peptides from two major seed storage proteins, glycinin and beta conglycinin, were analyzed. Proteins are composed of amino acids and therefore any isotopic labeling in peptides (protein fragments) analyzed by mass spectrometry will reflect labeling within the individual amino acids. This is important because proteins in plants are derived from multiple genomes that are located in different subcellular compartments (nucleus, mitochondria, and chloroplasts). Therefore methods to assess proteins will provide information on subcellular metabolism as well as protein turnover and dynamic events. Tandem mass spectrometers have not been used to evaluate metabolism in this way, though they are highly sensitive and hold great promise for isotopic labeling quantification. We systematically probed a variety of mass spectrometer energies, conditions and settings to evaluate performance and the potential extension of their use for this purpose. We compared natural abundance levels of isotopes predicted from metabolic simulations with those measured on the instrument. We also cultured developing soybeans with 13C substrates and inspected the enrichment of peptides. The findings indicate that there are optimal protocols to achieve the best mass spectrometer performance. Our results will enable better characterization of metabolism in plant tissues.Such methods are necessary to improve our understanding of plant function and guide rational metabolic engineering strategies in important crops like soybeans.

Technical Abstract: The cellular phenotype is the consequence of dynamic metabolic events that occur in a spacially dependent fashion. This spatial and temporal complexity presents challenges for investigating primary metabolism and improved methods to probe biochemical events such as amino acid biosynthesis may be needed to address these questions effectively. Isotopic labeling can provide insights to cellular phenomena and the recording of enriched amino acids due to metabolic events that are specific to location and time are recorded in the protein pool. Therefore proteins are an important readout for metabolism that can be assessed with modern day mass spectrometers. We examined the naturally abundant levels of isotopes in MS2 spectra that were obtained from tandem mass spectrometry under higher energy collision dissociation (HCD) and collision induced dissociation (CID) fragmentation, and compared the effect of energy levels on the fragmentation products. Developing soybean embryos that served as a source of biological material were cultured with [U-13C6]-glucose and proteins from unlabeled and 13C enriched biomass were used to assess spectrometer performance. Incomplete CID fragmentation resulted in MS1 spectra with a disproportionate amount of remaining heavier isotopes. HCD and CID-based fragmentation resulted in MS2 peptides that could be quantified precisely, but lower abundances gave more variable results and a deviation from simulated distributions was evident under all conditions. Although MS2 methods have the potential to provide information on the labeling of amino acids from peptides that are central to metabolism, their application to highly sensitive quantitative methods such as metabolic flux analysis may not be ready.