Protein turnover is an important aspect of the regulation of cellular processes for organisms when responding to developmental or environmental cues. The measurement of protein turnover in plants, in contrast to that of rapidly growing unicellular organismal cultures, is made more complicated by the high degree of amino acid recycling, resulting in significant transient isotope incorporation distributions that must be dealt with computationally for high throughput analysis to be practical. An algorithm in R, ProteinTurnover, was developed to calculate protein turnover with transient stable isotope incorporation distributions in a high throughput automated manner using high resolution MS and MS/MS proteomic analysis of stable isotopically labeled plant material. ProteinTurnover extracts isotopic distribution information from raw MS data for peptides identified by MS/MS from data sets of either isotopic label dilution or incorporation experiments. Variable isotopic incorporation distributions were modeled using binomial and beta-binomial distributions to deconvolute the natural abundance, newly synthesized/partial-labeled, and fully labeled peptide distributions. Maximum likelihood estimation was performed to calculate the distribution abundance proportion of old and newly synthesized peptides. The half-life or turnover rate of each peptide was calculated from changes in the distribution abundance proportions using nonlinear regression. We applied ProteinTurnover to obtain half-lives of proteins from enriched soluble and membrane fractions from Arabidopsis roots.
Bibliographical noteFunding Information:
A significant portion of the preliminary proteomics data was collected at the Center for Mass Spectrometry and Proteomics at the University of Minnesota, and we thank LeeAnn Higgins, Todd Markowski, and Bruce Witthuhn for their help with sample preparation and LC-MS/MS analysis. We were also assisted by the Minnesota Supercomputing Institute in turnover data analysis and thank John Chilton and Pratik Jagtap for their help with the Galaxy-P platform. The authors also thank Thomas F. McGowan, Sanford Weisberg, for helping with the preliminary design of the algorithm and Xiao-Yuan Yang for testing the early version of the algorithm. We are grateful for funding provided by the NSF Plant Genome Research Program grants DBI-0606666, IOS-0923960, IOS-1238812, and IOS-1400818 as well as NSF grant IOS-0820940, NIH grant GM067203, the University of Minnesota Informatics Institute, and by the Gordon and Margaret Bailey Endowment for Environmental Horticulture.
- Arabidopsis thaliana
- metabolic labeling
- protein turnover rates
- proteome dynamics
- stable isotope