Multitagging Proteomic Strategy to Estimate Protein Turnover Rates in Dynamic Systems

Abstract

Current techniques for quantitative proteomics focus mainly on measuring overall protein dynamics, which is the net result of protein synthesis and degradation. Understanding the rate of this synthesis/degradation is essential to fully appreciate cellular dynamics and bridge the gap between transcriptome and proteome data. Protein turnover rates can be estimated through "label-chase" experiments employing stable isotope-labeled precursors; however, the implicit assumption of steady-state in such analyses may not be applicable for many intrinsically dynamic systems. In this study, we present a novel extension of the "label-chase" concept using SILAC and a secondary labeling step with iTRAQ reagents to estimate protein turnover rates in Streptomyces coelicolor cultures undergoing transition from exponential growth to stationary phase. Such processes are of significance in Streptomyces biology as they pertain to the onset of synthesis of numerous therapeutically important secondary metabolites. The dual labeling strategy enabled decoupling of labeled peptide identification and quantification of degradation dynamics at MS and MS/MS scans respectively. Tandem mass spectrometry analysis of these multitagged proteins enabled estimation of degradation rates for 115 highly abundant proteins in S. coelicolor. We compared the rate constants obtained using this dual labeling approach with those from a SILAC-only analysis (assuming steady-state) and show that significant differences are generally observed only among proteins displaying considerable temporal dynamics and that the directions of these differences are largely consistent with theoretical predictions.

Determining Degradation and Synthesis Rates of Arabidopsis Proteins Using the Kinetics of Progressive 15N Labeling of Two-dimensional Gel-separated Protein Spots

“…With this alternative method of calculating K D , we can avoid the steadystate assumption, which is likely not appropriate for many proteins in our cell culture system. In this respect, our analysis is similar to a recent study using a dual labeling strategy of SILAC and iTRAQ to correct for changes in protein abundance in log growing bacteria (28). For the two regression methods, R values for the first order kinetics fit of the data varied, 76 of the 84 proteins met the thresholds defined by Yee et al (27), and 42 of the 84 proteins met the thresholds defined by Jayapal et al (28).…”

“…time 0) represented by abundance (A). Thus, we derived Equation 3, which is an approach similar to that of Jayapal et al (28).…”

“…In a manner analogous to Jayapal et al (28), K D can then be calculated by the natural log of NA protein remaining (1 Ϫ P deg ). …”

Stability of bacterial toggle switches is enhanced by cell-cycle lengthening by several orders of magnitude

“…DNA replication [30]. g Most of the bacterial proteins are very stable, with degradation rates: 1.4 × 10 −5 -5.6 × 10 −5 /s [25]. Some proteins have much higher degradation rates.…”

“…In bacteria the average protein lifetime is typically longer than the cell cycle [25], which causes the system to be far from equilibrium. Observations of fast growing Escherichia coli cells, which are able to divide as frequently as every 20 min, show an extreme level of cellular activity including continuous reproduction of genome [26], increased number of mRNAs, ribosomal RNAs, and proteins necessary to perform gene expression [27][28][29][30].…”