Genomic, transcriptomic and proteomic profiles of human aneuploid cells reveal that mRNA levels increase with gene copy number, but protein levels are partially compensated. Aneuploid cells also exhibit common alterations in several pathways, including an activation of autophagy.

No abstract

Lipid droplets (LDs) are important organelles in energy metabolism and lipid storage. Their cores are composed of neutral lipids that form a hydrophobic phase and are surrounded by a phospholipid monolayer that harbors specific proteins. Most well-established LD proteins perform important functions, particularly in cellular lipid metabolism. Morphological studies show LDs in close proximity to and interacting with membrane-bound cellular organelles, including the endoplasmic reticulum, mitochondria, peroxisomes, and endosomes. Because of these close associations, it is difficult to purify LDs to homogeneity. Consequently, the confident identification of bona fide LD proteins via proteomics has been challenging. Here, we report a methodology for LD protein identification based on mass spectrometry and protein correlation profiles. Using LD purification and quantitative, high-resolution mass spectrometry, we identified LD proteins by correlating their purification profiles to those of known LD proteins. Application of the protein correlation profile strategy to LDs isolated from Drosophila S2 cells led to the identification of 111 LD proteins in a cellular LD fraction in which 1481 proteins were detected. LD localization was confirmed in a subset of identified proteins via microscopy of the expressed proteins, thereby validating the approach. Among the identified LD proteins were both well-characterized LD proteins and proteins not previously known to be localized to LDs. Our method provides a high-confidence LD proteome of Drosophila cells and a novel approach that can be applied to identify LD proteins of other cell types and tissues. This analytic problem is particularly apparent for lipid droplets (LDs). LD architecture differs from that of other organelles in that they have hydrophobic cores of neutral lipids, primarily triacylglycerol and sterol esters, rather than an aqueous lumen. The LD core is shielded from the cytosol by a phospholipid monolayer, into which specific proteins are embedded (4 -6). The primary function of LDs is to store lipids as reservoirs of metabolic energy and membrane precursors. Additionally, LDs participate in host-pathogen interactions (7) and play central roles in numerous physiological processes and linked pathologies. For example, obesity is characterized by the overaccumulation of LDs in tissues, and LD accumulation in macrophages is a hallmark of atherosclerosis (8, 9). Given the diverse distribution of LDs in cell types and processes, their protein composition likely varies among cell types and is dynamic. Knowledge of LD compositions in different cell types will lay the foundation for functional characterization of this organelle and could help to reveal novel mechanisms contributing to metabolic diseases.LD protein composition has been analyzed in over a dozen studies that employed different methodologies to study different cell types ranging in evolutionary complexity from yeast to human (10 -21). These studies have provided long lists of putative LD proteins, but apart f...

Information on extracellular signals and conditions is often transduced by biological systems using cascades of protein phosphorylation that affect the activity of enzymes, the localization of proteins and gene expression. A model to study signal transduction is the response of the yeast Saccharomyces cerevisiae to osmotic changes as it shares many central themes with information processing modules in higher eukaryotes. Despite considerable progress in our understanding of this pathway, the scale and dynamics of this system have not been addressed systematically yet. Here, we report a comprehensive, quantitative, and time-resolved analysis using high-resolution mass spectrometry of phospho-proteome and proteome changes in response to osmotic stress in yeast. We identified 5534 unique phosphopeptide variants and 3383 yeast proteins. More than 15% of the detected phosphorylation site status changed more than two-fold within 5 minutes of treatment. Many of the corresponding phosphoproteins are involved in the early response to environmental stress. Surprisingly, we find that 158 regulated phosphorylation sites are potential substrates of basophilic kinases as opposed to the classical proline-directed MAP kinase network implicated in stress response mechanisms such as p38 and HOG pathways. Proteome changes reveal an increase in abundance of more than one hundred proteins after 20 min of salt stress. Many of these are involved in the cellular response to increased osmolarity, which include proteins used for glycerol production that is up-regulated to counterbalance the increased osmolarity of the salt containing growth medium. Although the overall relationship between our proteome and published mRNA changes is poor we find an excellent correlation between the subset of osmotic shock up-regulated proteins and their corresponding mRNA changes.

MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment