Benchmarking common quantification strategies for large-scale phosphoproteomics

Quantitative phosphoproteomics has become a standard method in molecular and cell biology. Here, the authors compare performance and parameters of phosphoproteome quantification by LFQ, SILAC, and MS2-/MS3-based TMT and introduce a TMT-adapted algorithm for calculating phosphorylation site stoichiometry

Proteomics Reveals Global Regulation of Protein SUMOylation by ATM and ATR Kinases during Replication Stress

Summary: The mechanisms that protect eukaryotic DNA during the cumbersome task of replication depend on the precise coordination of several post-translational modification (PTM)-based signaling networks. Phosphorylation is a well-known regulator of the replication stress response, and recently an essential role for SUMOs (small ubiquitin-like modifiers) has also been established. Here, we investigate the global interplay between phosphorylation and SUMOylation in response to replication stress. Using SUMO and phosphoproteomic technologies, we identify thousands of regulated modification sites. We find co-regulation of central DNA damage and replication stress responders, of which the ATR-activating factor TOPBP1 is the most highly regulated. Using pharmacological inhibition of the DNA damage response kinases ATR and ATM, we find that these factors regulate global protein SUMOylation in the protein networks that protect DNA upon replication stress and fork breakage, pointing to integration between phosphorylation and SUMOylation in the cellular systems that protect DNA integrity. : Munk et al. use mass spectrometry-based proteomics to analyze the interplay between SUMOylation and phosphorylation in replication stress. They analyze changes in the SUMO and phosphoproteome after MMC and hydroxyurea treatments and find that the DNA damage response kinases ATR and ATM globally regulate SUMOylation upon replication stress and fork breakage. Keywords: Replication stress, quantitative proteomics, phosphoproteomics, SUMO, ATR, ATM, TOPBP1, MMC, kinase inhibitors, hydroxyure

Proteomics insights into DNA damage response and translating this knowledge to clinical strategies

Genomic instability is a critical driver in the process of cancer formation. At the same time, inducing DNA damage by irradiation or genotoxic compounds constitutes a key therapeutic strategy to kill fast‐dividing cancer cells. Sensing of DNA lesions initiates a complex set of signalling pathways, collectively known as the DNA damage response (DDR). Deciphering DDR signalling pathways with high‐throughput technologies could provide insights into oncogenic transformation, metastasis formation and therapy responses, and could build a basis for better therapeutic interventions in cancer treatment. Mass spectrometry (MS)‐based proteomics emerged as a method of choice for global studies of proteins and their posttranslational modifications (PTMs). MS‐based studies of the DDR have aided in delineating DNA damage‐induced signalling responses. Those studies identified changes in abundance, interactions and modification of proteins in the context of genotoxic stress. Here we review ground‐breaking MS‐based proteomics studies, which analysed changes in protein abundance, protein‐protein and protein‐DNA interactions, phosphorylation, acetylation, ubiquitylation, SUMOylation and Poly(ADP‐ribose)ylation (PARylation) in the DDR. Finally, we provide an outlook on how proteomics studies of the DDR could aid clinical developments on multiple levels

Recent findings and technological advances in phosphoproteomics for cells and tissues.

Site-specific phosphorylation is a fast and reversible covalent post-translational modification that is tightly regulated in cells. The cellular machinery of enzymes that write, erase and read these modifications (kinases, phosphatases and phospho-binding proteins) is frequently deregulated in different diseases, including cancer. Large-scale studies of phosphoproteins – termed phosphoproteomics – strongly rely on the use of high-performance mass spectrometric instrumentation. This powerful technology has been applied to study a great number of phosphorylation-based phenotypes. Nevertheless, many technical and biological challenges have to be overcome to identify biologically relevant phosphorylation sites in cells and tissues. This review describes different technological strategies to identify and quantify phosphorylation sites with high accuracy, without significant loss of analysis speed and reproducibility in tissues and cells. Moreover, computational tools for analysis, integration and biological interpretation of phosphorylation events are discussed

Purine metabolism.

<p>Upregulated enzymes in green, downregulated enzymes in red. Upregulated metabolites in dark red, downregulated metabolites in blue.</p

SAMe centered pathways.

<p>Upregulated enzymes in green, downregulated enzymes in red. Upregulated metabolites in dark red, downregulated metabolites in blue.</p

Network of differentially regulated metabolites.

<p>Orange, Blue and Grey boxes indicate metabolites (mean of 5 replicates) upregulated, downregulated, and unchanged in cisplatin treatment, respectively (ANOVA p<0.01 and fold change 1.1); metabolites without a box were not detected. Numbers left (cisplatin) and right (control) under boxes indicate the corresponding intensities in MS-data. Results after 4 hours upper panel) and 8 hours (lower panel) are shown.</p

Pyrimidine metabolism.

<p>Upregulated enzymes in green, downregulated enzymes in red. Upregulated metabolites in dark red, downregulated metabolites in blue.</p

Integrated signaling network.

<p>(<b>A</b>) Schematic representation of metabolomics and transcriptomics data integration leading to identification of common signaling networks related to nucleotide metabolism, SAMe pathways, polyamine pathways and urea cycle, and arginine & proline metabolism. (<b>B</b>) Integrated signaling network of metabolic enzymes and metabolites obtained with Ingenuity pathway analysis. Clusters of significantly enriched canonical pathways and related enzymes and metabolites are highlighted. Upregulated enzymes in green, downregulated enzymes in red. Upregulated metabolites in dark red, downregulated metabolites in blue.</p

Differentially regulated metabolic enzymes.

<p>(<b>A</b>) Heatmap indicating metabolic enzymes obtained from Cytoscape metabolic signaling network (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076476#pone.0076476.s004" target="_blank">Fig. S4</a>, highlighted in blue; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076476#pone.0076476.s007" target="_blank">Table S2</a>), differentially regulated after 8 h of cisplatin treatment and enriched metabolic pathways within the dataset. (<b>B</b>) Regulation of metabolic enzymes by the transcription factor p53 obtained with Ingenuity pathway analysis.</p