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.

Many biological processes are intrinsically dynamic, incurring profound changes at both molecular and physiological levels. Systems analyses of such processes incorporating large-scale transcriptome or proteome profiling can be quite revealing. Although consistency between mRNA and proteins is often implicitly assumed in many studies, examples of divergent trends are frequently observed. Here, we present a comparative transcriptome and proteome analysis of growth and stationary phase adaptation in Streptomyces coelicolor, taking the time-dynamics of process into consideration. These processes are of immense interest in microbiology as they pertain to the physiological transformations eliciting biosynthesis of many naturally occurring therapeutic agents. A shotgun proteomics approach based on mass spectrometric analysis of isobaric stable isotope labeled peptides (iTRAQ™) enabled identification and rapid quantification of approximately 14% of the theoretical proteome of S. coelicolor. Independent principal component analyses of this and DNA microarray-derived transcriptome data revealed that the prominent patterns in both protein and mRNA domains are surprisingly well correlated. Despite this overall correlation, by employing a systematic concordance analysis, we estimated that over 30% of the analyzed genes likely exhibited significantly divergent patterns, of which nearly one-third displayed even opposing trends. Integrating this data with biological information, we discovered that certain groups of functionally related genes exhibit mRNA-protein discordance in a similar fashion. Our observations suggest that differences between mRNA and protein synthesis/degradation mechanisms are prominent in microbes while reaffirming the plausibility of such mechanisms acting in a concerted fashion at a protein complex or sub-pathway level.

Background: The genomes of Streptomyces coelicolor and Streptomyces lividans bear a considerable degree of synteny. While S. coelicolor is the model streptomycete for studying antibiotic synthesis and differentiation, S. lividans is almost exclusively considered as the preferred host, among actinomycetes, for cloning and expression of exogenous DNA. We used whole genome microarrays as a comparative genomics tool for identifying the subtle differences between these two chromosomes.

Background: A small "sigma-like" protein, AfsS, pleiotropically regulates antibiotic biosynthesis in Streptomyces coelicolor. Overexpression of afsS in S. coelicolor and certain related species causes antibiotic stimulatory effects in the host organism. Although recent studies have uncovered some of the upstream events activating this gene, the mechanisms through which this signal is relayed downstream leading to the eventual induction of antibiotic pathways remain unclear.

Streptomyces spp. produce a variety of valuable secondary metabolites, which are regulated in a spatio-temporal manner by a complex network of inter-connected gene products. Using a compilation of genome-scale temporal transcriptome data for the model organism, Streptomyces coelicolor, under different environmental and genetic perturbations, we have developed a supervised machine-learning method for operon prediction in this microorganism. We demonstrate that, using features dependent on transcriptome dynamics and genome sequence, a support vector machines (SVM)-based classification algorithm can accurately classify >90% of gene pairs in a set of known operons. Based on model predictions for the entire genome, we verified the co-transcription of more than 250 gene pairs by RT-PCR. These results vastly increase the database of known operons in S. coelicolor and provide valuable information for exploring gene function and regulation to harness the potential of this differentiating microorganism for synthesis of natural products.