Dataset Information

Global subcellular characterization of protein degradation using quantitative proteomics.

ABSTRACT: Protein degradation provides an important regulatory mechanism used to control cell cycle progression and many other cellular pathways. To comprehensively analyze the spatial control of protein degradation in U2OS osteosarcoma cells, we have combined drug treatment and SILAC-based quantitative mass spectrometry with subcellular and protein fractionation. The resulting data set analyzed more than 74,000 peptides, corresponding to ~5000 proteins, from nuclear, cytosolic, membrane, and cytoskeletal compartments. These data identified rapidly degraded proteasome targets, such as PRR11 and highlighted a feedback mechanism resulting in translation inhibition, induced by blocking the proteasome. We show this is mediated by activation of the unfolded protein response. We observed compartment-specific differences in protein degradation, including proteins that would not have been characterized as rapidly degraded through analysis of whole cell lysates. Bioinformatic analysis of the entire data set is presented in the Encyclopedia of Proteome Dynamics, a web-based resource, with proteins annotated for stability and subcellular distribution.

SUBMITTER: Larance M

PROVIDER: S-EPMC3591657 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Global subcellular characterization of protein degradation using quantitative proteomics.

Larance Mark M Ahmad Yasmeen Y Kirkwood Kathryn J KJ Ly Tony T Lamond Angus I AI

Molecular & cellular proteomics : MCP 20121212 3

Protein degradation provides an important regulatory mechanism used to control cell cycle progression and many other cellular pathways. To comprehensively analyze the spatial control of protein degradation in U2OS osteosarcoma cells, we have combined drug treatment and SILAC-based quantitative mass spectrometry with subcellular and protein fractionation. The resulting data set analyzed more than 74,000 peptides, corresponding to ~5000 proteins, from nuclear, cytosolic, membrane, and cytoskeletal ...[more]

PMID: 23242552

Similar Datasets

Project description:Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multidimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than 1300 proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis, and nonpancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the nonpancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.

Dataset Information

Global subcellular characterization of protein degradation using quantitative proteomics.

Publications

Global subcellular characterization of protein degradation using quantitative proteomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets