ROCK1 is a potential combinatorial drug target for BRAF mutant melanoma.

Treatment of BRAF mutant melanomas with specific BRAF inhibitors leads to tumor remission. However, most patients eventually relapse due to drug resistance. Therefore, we designed an integrated strategy using (phospho)proteomic and functional genomic platforms to identify drug targets whose inhibition sensitizes melanoma cells to BRAF inhibition. We found many proteins to be induced upon PLX4720 (BRAF inhibitor) treatment that are known to be involved in BRAF inhibitor resistance, including FOXD3 and ErbB3. Several proteins were down-regulated, including Rnd3, a negative regulator of ROCK1 kinase. For our genomic approach, we performed two parallel shRNA screens using a kinome library to identify genes whose inhibition sensitizes to BRAF or ERK inhibitor treatment. By integrating our functional genomic and (phospho)proteomic data, we identified ROCK1 as a potential drug target for BRAF mutant melanoma. ROCK1 silencing increased melanoma cell elimination when combined with BRAF or ERK inhibitor treatment. Translating this to a preclinical setting, a ROCK inhibitor showed augmented melanoma cell death upon BRAF or ERK inhibition in vitro. These data merit exploration of ROCK1 as a target in combination with current BRAF mutant melanoma therapies.

Protein acetylation affects acetate metabolism, motility and acid stress response in Escherichia coli.

Although protein acetylation is widely observed, it has been associated with few specific regulatory functions making it poorly understood. To interrogate its functionality, we analyzed the acetylome in Escherichia coli knockout mutants of cobB, the only known sirtuin-like deacetylase, and patZ, the best-known protein acetyltransferase. For four growth conditions, more than 2,000 unique acetylated peptides, belonging to 809 proteins, were identified and differentially quantified. Nearly 65% of these proteins are related to metabolism. The global activity of CobB contributes to the deacetylation of a large number of substrates and has a major impact on physiology. Apart from the regulation of acetyl-CoA synthetase, we found that CobB-controlled acetylation of isocitrate lyase contributes to the fine-tuning of the glyoxylate shunt. Acetylation of the transcription factor RcsB prevents DNA binding, activating flagella biosynthesis and motility, and increases acid stress susceptibility. Surprisingly, deletion of patZ increased acetylation in acetate cultures, which suggests that it regulates the levels of acetylating agents. The results presented offer new insights into functional roles of protein acetylation in metabolic fitness and global cell regulation.

Reorganized PKA-AKAP associations in the failing human heart.

Here we reveal that the characterization of large-scale re-arrangements of signaling scaffolds induced by heart failure can serve as a novel concept to identify more specific therapeutic targets. In the mammalian heart, the cAMP pathway, with the cAMP-dependent protein kinase (PKA) in a central role, acts directly downstream of adrenergic receptors to mediate cardiac contractility and rhythm. Heart failure, characterized by severe alterations in adrenergic stimulation is, amongst other interventions, often treated with ß-blockers. Contrasting results, however, have shown both beneficial and detrimental effects of decreased cAMP levels in failing hearts. We hypothesize that the origin of this behavior lies in the complex spatiotemporal organization of the regulatory subunit of PKA (PKA-R), which associates tightly with various A-kinase anchoring proteins (AKAPs) to specifically localize PKA's activity. Using chemical proteomics directly applied to human patient and control heart tissue we demonstrate that the association profile of PKA-R with several AKAPs is severely altered in the failing heart, for instance effecting the interaction between PKA and the novel AKAP SPHKAP was 6-fold upregulated upon failing heart conditions. Also a significant increase in captured cGMP-dependent protein kinase (PKG) and phosphodiesterase 2 (PDE2) was observed. The observed altered profiles can already explain many aspects of the aberrant cAMP-response in the failing human heart, validating that this dataset may provide a resource for several novel, more specific, treatment options. This article is part of a Special Issue entitled "Local Signaling in Myocytes".

Deep proteome profiling of circulating granulocytes reveals bactericidal/permeability-increasing protein as a biomarker for severe atherosclerotic coronary stenosis.

Coronary atherosclerosis represents the major cause of death in Western societies. As atherosclerosis typically progresses over years without giving rise to clinical symptoms, biomarkers are urgently needed to identify patients at risk. Over the past decade, evidence has accumulated suggesting cross-talk between the diseased vasculature and cells of the innate immune system. We therefore employed proteomics to search for biomarkers associated with severe atherosclerotic coronary lumen stenosis in circulating leukocytes. In a two-phase approach, we first performed in-depth quantitative profiling of the granulocyte proteome on a small pooled cohort of patients suffering from chronic (sub)total coronary occlusion and matched control patients using stable isotope peptide labeling, two-dimensional LC-MS/MS and data-dependent decision tree fragmentation. Over 3000 proteins were quantified, among which 57 candidate biomarker proteins remained after stringent filtering. The most promising biomarker candidates were subsequently verified in the individual samples of the discovery cohort using label-free, single-run LC-MS/MS analysis, as well as in an independent verification cohort of 25 patients with total coronary occlusion (CTO) and 19 matched controls. Our data reveal bactericidal/permeability-increasing protein (BPI) as a promising biomarker for severe atherosclerotic coronary stenosis, being down-regulated in circulating granulocytes of CTO patients.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics.

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

Applications of stable isotope dimethyl labeling in quantitative proteomics.

Mass spectrometry has proven to be an indispensable tool for protein identification, characterization, and quantification. Among the possible methods in quantitative proteomics, stable isotope labeling by using reductive dimethylation has emerged as a cost-effective, simple, but powerful method able to compete at any level with the present alternatives. In this review, we briefly introduce experimental and software methods for proteome analysis using dimethyl labeling and provide a comprehensive overview of reported applications in the analysis of (1) differential protein expression, (2) posttranslational modifications, and (3) protein interactions.

Improved label-free LC-MS analysis by wavelet-based noise rejection.

Label-free LC-MS analysis allows determining the differential expression level of proteins in multiple samples, without the use of stable isotopes. This technique is based on the direct comparison of multiple runs, obtained by continuous detection in MS mode. Only differentially expressed peptides are selected for further fragmentation, thus avoiding the bias toward abundant peptides typical of data-dependent tandem MS. The computational framework includes detection, alignment, normalization and matching of peaks across multiple sets, and several software packages are available to address these processing steps. Yet, more care should be taken to improve the quality of the LC-MS maps entering the pipeline, as this parameter severely affects the results of all downstream analyses. In this paper we show how the inclusion of a preprocessing step of background subtraction in a common laboratory pipeline can lead to an enhanced inclusion list of peptides selected for fragmentation and consequently to better protein identification.

Quantification of membrane proteins using nonspecific protease digestions.

We present a mass spectrometry-based method for the identification and quantification of membrane proteins using the low-specificity protease Proteinase K, at very high pH, to digest proteins isolated by a modified SDS-PAGE protocol. The resulting peptides are modified with a fragmentation-directing isotope labeled tag. We apply the method to quantify differences in membrane protein expression of Bacillus subtilis grown in the presence or absence of glucose.

Wavelet-based method for noise characterization and rejection in high-performance liquid chromatography coupled to mass spectrometry.

We present a new method for rejecting noise from HPLC-MS data sets. The algorithm reveals peptides at low concentrations by minimizing both the chemical and the random noise. The goal is reached through a systematic approach to characterize and remove the background. The data are represented as two-dimensional maps, in order to optimally exploit the complementary dimensions of separation of the peptides offered by the LC-MS technique. The virtual chromatograms, reconstructed from the spectrographic data, have proved to be more suitable to characterize the noise than the raw mass spectra. By means of wavelet analysis, it was possible to access both the chemical and the random noise, at different scales of the decomposition. The novel approach has proved to efficiently distinguish signal from noise and to selectively reject the background while preserving low-abundance peptides.

Deconvolution of overlapping isotopic clusters improves quantification of stable isotope-labeled peptides.

High-resolution mass spectrometry and the use of stable isotopes have greatly improved our ability to quantify proteomes. Typically, the relative abundance of peptides is estimated by identifying the isotopic clusters and by comparing the peak intensities of peptide pairs. However, when the mass shift between the labeled peptides is small, there can be the possibility for overlap of the isotopic clusters which will hamper quantification accuracy with a typical upwards bias for the heavier peptide. Here, we investigated the impact of the overlapping peak issue with respect to dimethyl based quantification and we confirmed there can be need for correction. In addition, we present a tool that can correct overlapping issues when they arise which is based on modeling isotopic distributions. We demonstrate that our approach leads to improved accuracy and precision of protein quantification.