Proteins with High Turnover Rate in Barley Leaves Estimated by Proteome Analysis Combined with in Planta Isotope Labeling    

Abstract

Protein turnover is a key component in cellular homeostasis; however, there is little quantitative information on degradation kinetics for individual plant proteins. We have used 15 N labeling of barley (Hordeum vulgare) plants and gas chromatography-mass spectrometry analysis of free amino acids and liquid chromatography-mass spectrometry analysis of proteins to track the enrichment of 15 N into the amino acid pools in barley leaves and then into tryptic peptides derived from newly synthesized proteins. Using information on the rate of growth of barley leaves combined with the rate of degradation of 14 N-labeled proteins, we calculate the turnover rates of 508 different proteins in barley and show that they vary by more than 100-fold. There was approximately a 9-h lag from label application until 15 N incorporation could be reliably quantified in extracted peptides. Using this information and assuming constant translation rates for proteins during the time course, we were able to quantify degradation rates for several proteins that exhibit half-lives on the order of hours. Our workflow, involving a stringent series of mass spectrometry filtering steps, demonstrates that 15 N labeling can be used for large-scale liquid chromatography-mass spectrometry studies of protein turnover in plants. We identify a series of abundant proteins in photosynthesis, photorespiration, and specific subunits of chlorophyll biosynthesis that turn over significantly more rapidly than the average protein involved in these processes. We also highlight a series of proteins that turn over as rapidly as the well-known D1 subunit of photosystem II. While these proteins need further verification for rapid degradation in vivo, they cluster in chlorophyll and thiamine biosynthesis.

Protein Degradation Rate in Arabidopsis thaliana Leaf Growth and Development

“…However, despite these advantages, the LPF approach employed here can be affected by a lag before new protein synthesis can be effectively distinguishable from previously present (NA) proteins. The lag is overcome as soon as a 10 to 15% labeling level is reached in major amino acid pools (Nelson et al, 2014). The three different leaves did show some differences in 15 N labeling efficiency (Supplemental Figure 4).…”

“…As a result, studies have assessed the utility of various metabolic labels ( 2 H, 13 C, and 15 N) for the purpose in plants (Yang et al, 2010;Chen et al, 2011;Li et al, 2012). Recently, metabolic labeling with inorganic 15 N has been the most commonly used stable isotope incorporation technique to define degradation or turnover rates of organelle proteins in Arabidopsis cell culture (Nelson et al, 2013), protease targets in Arabidopsis leaf mitochondria (Huang et al, 2015;Li et al, 2016), ;500 proteins in barley (Hordeum vulgare) leaves (Nelson et al, 2014), ;200 membrane and microsomal fraction proteins from Arabidopsis roots (Fan et al, 2016), and ;500 Medicago truncatula leaf and root proteins during drought and recovery from drought (Lyon et al, 2016). One study has also followed selected protein degradation rates using 13 CO 2 in Arabidopsis leaves (Ishihara et al, 2015).…”

Leaf Starch Turnover Occurs in Long Days and in Falling Light at the End of the Day

“…Although transcript levels for many enzymes of starch degradation rise with time after dawn, there is evidence for enzymes including glucan, water dikinase, a-amylase 3 (AMY3), and the glucanotransferase DPE2 that the amount of protein varies very little over the day-night cycle Lu et al, 2005;Yu et al, 2005;Skeffington et al, 2014). In general terms, marked diel fluctuations in amounts of enzymes of primary metabolism are unlikely because of their high abundance and long half-lives (Gibon et al, 2004;Piques et al, 2009;Baerenfaller et al, 2012;Nelson et al, 2014). An intriguing exception is the major starch-degrading enzyme BAM3, which was recently shown to have a very short half-life of only 0.43 d (Li et al, 2017).…”

A Method of Accounting for Enzyme Costs in Flux Balance Analysis Reveals Alternative Pathways and Metabolite Stores in an Illuminated Arabidopsis Leaf

“…An alternative approach is to empirically measure the rate of synthesis of each enzyme in the system by analyzing steady-state protein abundance and protein turnover rates. Recently, a combination of 15 N labeling and proteomics was used to acquire such data for individual proteins (Li et al, 2012;Nelson et al, 2014), and in the future, it may be possible to use such methods to obtain reliable data about protein synthesis rates in absolute terms for the majority of the individual isozymes that make up the metabolic network. Nevertheless, the acquisition of such data are a substantial effort, and although the use of experimentally defined reaction weightings may provide a biologically verified result, it will also tend to lead to model predictions that minimize the use of alternative pathways, because the optimization algorithm will favor the metabolic route(s) with the lowest cost given the defined weightings.…”