Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Time-Resolved Proteomics Extends Ribosome Profiling-Based Measurements of Protein Synthesis Dynamics

ABSTRACT: Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics.

ORGANISM(S): Homo sapiens

PROVIDER: GSE69047 | GEO | 2017/05/18

SECONDARY ACCESSION(S): PRJNA284399

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Time-Resolved Proteomics Extends Ribosome Profiling-Based Measurements of Protein Synthesis Dynamics

Project description:Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics. Timecourse experiment with six points over 48hr after bortezomib exposure in MM.1S myeloma cells. mRNA-seq and ribosome profiling data at each time point.

2017-05-18 | E-GEOD-69047 | biostudies-arrayexpress

Global response to chemotherapy-induced apoptosis

Project description:Here we use an integrated systems-level examination of transcription, translation, and proteolysis to explore how cancer cells struggle with a chemotherapeutic drug prior to succumbing to apoptosis. As a model system we study myeloma cells exposed to the proteasome inhibitor bortezomib, a first-line clinical treatment. Despite robust transcriptional changes, unbiased quantitative proteomics detects production of only a few critical anti-apoptotic proteins against a background of general translation inhibition. Ribosome profiling further reveals potential translational regulation of stress response genes following bortezomib treatment. Once the apoptotic machinery is engaged, degradation by caspases is largely independent of changes at the transcriptional level. Moreover, previously uncharacterized non-caspase proteolytic events also participate in cellular deconstruction. As suggested by these data, we find that inhibition of the anti-apoptotic response regulator HSF1 promotes cell death by bortezomib. Thus, monitoring global cellular dynamics after chemotherapy offers in-depth insight into apoptosis and can also guide potential therapeutic combinations. We examined MM1.S myeloma cells exposed to 20 nM bortezomib across a time course with independent samples of poly(A) mRNA and ribosome footprints isolated at each of six time points (0h (untreated), 1.5h, 3h, 6h, 9h, 12h). Sequencing was performed on a Illumina HiSeq 2000 with single-end.

2013-10-17 | E-GEOD-48785 | biostudies-arrayexpress

PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration

Project description:An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. We present the generation of a protein synthesis-based database from deep sequencing of ribosome-protected mRNA fragments. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse respectively and enables proteome-wide detection of 5’-extended proteoforms, uORF translation and near-cognate translation start sites.

2014-12-01 | GSE58207 | GEO

Positional proteomics reveals differences in N-terminal proteoform stability

Project description:To understand the impact of alternative translation initiation on a proteome, we performed the first study on protein turnover using positional proteomics and ribosome profiling to distinguish between N-terminal proteoforms of individual genes. Overall, we monitored the stability of 1,941 human N-terminal proteoforms, including 147 N-terminal proteoform pairs that originate from alternative translation initiation, alternative splicing or incomplete processing of the initiator methionine. Ribosome profiling of lactimidomycin and cycloheximide treated human Jurkat T-lymphocytes

2015-10-23 | E-GEOD-74279 | biostudies-arrayexpress

Profiling Ssb-Nascent Chain Interactions Reveals Principles of Hsp70-Assisted Folding

Project description:The yeast Hsp70 chaperone Ssb interacts with ribosomes and nascent chains to co-translationally assist protein folding. Here, we present a proteome-wide analysis of Hsp70 function during translation, based on in vivo selective ribosome profiling, that reveals mechanistic principles coordinating translation with chaperone-assisted protein folding. Ssb binds most cytosolic, nuclear, and mitochondrial proteins and a subset of ER proteins, supporting its general chaperone function. Position-resolved analysis of Ssb engagement reveals compartment- and protein-specific nascent chain binding profiles that are coordinated by emergence of positively charged peptide stretches enriched in aromatic amino acids. Ssbs’ function is temporally coordinated by RAC but independent from NAC. Analysis of ribosome footprint densities along orfs reveals that ribosomes translate faster at times of Ssb binding. This is coordinated by biases in mRNA secondary structure, and codon usage as well as the action of Ssb, suggesting chaperones may allow higher protein synthesis rates by actively coordinating protein synthesis with co-translational folding.

2017-07-17 | GSE93830 | GEO

Mutant huntingtin stalls ribosomes and represses protein synthesis in a cellular model of Huntington disease [Ribo-seq]

Project description:The polyglutamine expansion of huntingtin (mHtt) causes Huntington disease (HD) and neurodegeneration, but the mechanisms remain unclear. Here, we found that mHtt promotes ribosome stalling and suppresses protein synthesis. Depletion of mHtt enhances protein synthesis and increases the speed of ribosome translocation, while mHtt directly inhibits protein synthesis in vitro. We found interactions of ribosomal proteins and translating ribosomes with mHtt. High-resolution global ribosome footprint profiling (Ribo-Seq) and mRNA-Seq indicated a widespread shift in ribosome occupancy toward the 5’ and 3’ end and unique single-codon pauses on selected mRNA targets in HD cells, compared to controls. Thus, mHtt impedes ribosomal translocation during translation by promoting ribosome stalling, a novel mechanistic defect that can be exploited for HD therapeutics.

2020-07-22 | GSE146674 | GEO

Mutant huntingtin stalls ribosomes and represses protein synthesis in a cellular model of Huntington disease [RNA-seq]

2020-07-22 | GSE146673 | GEO

Mutant huntingtin stalls ribosomes and represses protein synthesis in a cellular model of Huntington disease (mRNA-Seq)

2021-02-13 | GSE166695 | GEO

Tau drives translational selectivity by interacting with ribosomal proteins

Project description:There is a fundamental gap in understanding the consequences of tau-ribosome interactions. Tau oligomers and filaments hinder protein synthesis in vitro, and they associate strongly with ribosomes in vivo. Here, we investigated the consequences of tau interactions with ribosomes in vivo and in human brain tissues to identify tau as a direct modulator of ribosomal selectivity. We performed microarrays and nascent proteomics to measure changes in protein synthesis using rTg4510 tau transgenic mice. We determined that tau expression differentially shifts the transcriptome and the proteome and that the synthesis of ribosomal proteins is reversibly dependent on tau levels. We further extended these results to human brains and show that tau pathologically interacts with ribosomal protein S6 (rpS6 or S6). Consequently, synthesis of ribosomal proteins coded by 5’TOP-mRNAs was reduced under tauopathic conditions in Alzheimer’s disease brains. Our data establish tau as a driver of RNA translation selectivity. Moreover, considering that regulation of protein synthesis is critical to learning and memory, aberrant tau-ribosome interactions in disease could explain the linkage between virtually every tauopathy and cognitive impairment and memory decline.

2019-03-30 | GSE121264 | GEO

Objective molecular criteria for oocyte quality and developmental potential: a proteomic study in inbred mice

Project description:Current understanding of oocyte quality and developmental potential maintains that stochastic or epigenetic processes modulate the action of determinants that are laid in the oocyte during oogenesis. These determinants are considered to be rather conserved across mice, leading different strains of mice to be used interchangeably. We challenged this assumption and studied the relationship between oocyte composition and developmental quality in four inbred strains of mice, namely 129Sv, C57Bl/6, C3H/HeN and DBA/2J. These oocytes showed large variability developmental competence and embryo quality irrespective of the developmental stimulus (fertilization, somatic cell nuclear transfer, parthenogenesis). To unravel the molecular basis of the observed phenotypes we applied state-of-the-art proteomics (SILAC LC-MS/MS) combined with transcriptomics (RNA deep sequencing). We quantified 1839 proteins and 20413 transcripts simultaneously in oocytes of all four strains. The proteome and the transcriptome had little correlation with each other, highlighting the importance of proteomic quantifications in embryology. We found that proteins that were most variably expressed between oocytes from different strains mainly relate to oocyte biology, ribosome and RNA biogenesis as well as embryo differentiation and chromatin remodelling. Thus, different strains of mice should not be used interchangeably in biology when tackling questions about oogenesis and early development.

2016-06-18 | E-MTAB-5013 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data