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.