Cells typically respond quickly to stress, altering their metabolism to compensate. In mammalian cells, stress signaling usually leads to either cell-cycle arrest or apoptosis, depending on the severity of the insult and the ability of the cell to recover. Stress also often leads to reorganization of nuclear architecture, reflecting the simultaneous inhibition of major nuclear pathways (e.g., replication and transcription) and activation of specific stress responses (e.g., DNA repair). In this review, we focus on how two nuclear organelles, the nucleolus and the Cajal body, respond to stress. The nucleolus senses stress and is a central hub for coordinating the stress response. We review nucleolar function in the stress-induced regulation of p53 and the specific changes in nucleolar morphology and composition that occur upon stress. Crosstalk between nucleoli and CBs is also discussed in the context of stress responses.

Measuring the properties of endogenous cell proteins, such as expression level, subcellular localization, and turnover rates, on a whole proteome level remains a major challenge in the postgenome era. Quantitative methods for measuring mRNA expression do not reliably predict corresponding protein levels and provide little or no information on other protein properties. Here we describe a combined pulse-labeling, spatial proteomics and data analysis strategy to characterize the expression, localization, synthesis, degradation, and turnover rates of endogenously expressed, untagged human proteins in different subcellular compartments. Using quantitative mass spectrometry and stable isotope labeling with amino acids in cell culture, a total of 80,098 peptides from 8,041 HeLa proteins were quantified, and their spatial distribution between the cytoplasm, nucleus and nucleolus determined and visualized using specialized software tools developed in PepTracker. Using information from ion intensities and rates of change in isotope ratios, protein abundance levels and protein synthesis, degradation and turnover rates were calculated for the whole cell and for the respective cytoplasmic, nuclear, and nucleolar compartments. Expression levels of endogenous HeLa proteins varied by up to seven orders of magnitude. The average turnover rate for HeLa proteins was ∼20 h. Turnover rate did not correlate with either molecular weight or net charge, but did correlate with abundance, with highly abundant proteins showing longer than average half-lives. Fast turnover proteins had overall a higher frequency of PEST motifs than slow turnover proteins but no general correlation was observed between amino or carboxyl terminal amino acid identities and turnover rates. A subset of proteins was identified that exist in pools with different turnover rates depending on their subcellular localization. This strongly correlated with subunits of large, multiprotein complexes, suggesting a general mechanism whereby their assembly is controlled in a different subcellular location to their main site of function.

The identification of interaction partners in protein complexes is a major goal in cell biology. Here we present a reliable affinity purification strategy to identify specific interactors that combines quantitative SILAC-based mass spectrometry with characterization of common contaminants binding to affinity matrices (bead proteomes). This strategy can be applied to affinity purification of either tagged fusion protein complexes or endogenous protein complexes, illustrated here using the well-characterized SMN complex as a model. GFP is used as the tag of choice because it shows minimal nonspecific binding to mammalian cell proteins, can be quantitatively depleted from cell extracts, and allows the integration of biochemical protein interaction data with in vivo measurements using fluorescence microscopy. Proteins binding nonspecifically to the most commonly used affinity matrices were determined using quantitative mass spectrometry, revealing important differences that affect experimental design. These data provide a specificity filter to distinguish specific protein binding partners in both quantitative and nonquantitative pull-down and immunoprecipitation experiments.

Arginine methylation is a post-translational modification that results in the formation of asymmetrical and symmetrical dimethylated arginines (a- and sDMA). This modification is catalyzed by type I and II protein-arginine methyltransferases (PRMT), respectively. The two major enzymes PRMT1 (type I) and PRMT5 (type II) preferentially methylate arginines located in RG-rich clusters. Arginine methylation is a common modification, but the reagents for detecting this modification have been lacking. Thus, fewer than 20 proteins have been identified in the last 40 years as containing dimethylated arginines. We have generated previously four arginine methyl-specific antibodies; ASYM24 and ASYM25 are specific for aDMA, whereas SYM10 and SYM11 recognize sDMA. All of these antibodies were generated by using peptides with aDMA or sDMA in the context of different RG-rich sequences. HeLa cell extracts were used to purify the protein complexes recognized by each of the four antibodies, and the proteins were identified by microcapillary reverse-phase liquid chromatography coupled on line with electrospray ionization tandem mass spectrometry. The analysis of two tandem mass spectra for each methyl-specific antibody resulted in the identification of over 200 new proteins that are putatively arginine-methylated. The major protein complexes that were purified include components required for pre-mRNA splicing, polyadenylation, transcription, signal transduction, and cytoskeleton and DNA repair. These findings provide a basis for the identification of the role of arginine methylation in many cellular processes.

An experimental data handling system has been created as an update to the previous Nucleolar Proteome Database (NOPdb3.0: http://www.lamondlab.com/NOPdb3.0/). This updated system is able to manage large data sets identified by multiple mass spectrometry and has been used to analyse highly purified preparations of human nucleoli from different cell lines. The newly created application includes a dynamic relational database, which is kept up to date by laboratory staff. The data are further annotated with information from specific external sources on the web, including the IPI and Gene Ontology databases. In addition, an Application Programming Interface provides external users with a portal to link into the nucleolar proteome database and hence, gain access to continually updated results. From the initial ∼700 human proteins identified in the previous iteration of the NOPdb, we have now identified over 50 000 peptides contained in over 4500 human proteins from purified nucleoli, providing enhanced coverage of the nucleolar proteome.