Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systematic identification of changes in the yeast protein interaction network in response to environmental, chemical, and genetic perturbation [interactome data]

ABSTRACT: To understand the principles underlying protein-protein interaction (PPI) complex changes in response to external perturbations, we created a highly multiplexed version of the murine dihydrofolate reductase protein complementation assay (mDHFR PCA) in Saccharomyces cerevisiae, allowing quantitative PPI complex profiling in vivo. We investigated the effects of 14 different conditions (including small molecules, abiotic stress factors, and nutrient composition) on a total of 1383 PPIs. More than half of PPIs (758) were found to be variable, and their Gene Ontology (GO) annotations were found to be informative of both the nature of the perturbation within each condition, as well as the overall variability of the interactions across conditions. Many perturbations triggered network changes characterized by large connected modules centered around highly connected proteins ('hubs'), suggesting that cellular control of a few proteins (e.g., by mRNA levels) can induce widespread PPI remodeling. Under a diauxic shift from glucose to ethanol as the main carbon source, we found a striking relationship between PPI changes measured by our assay and those predicted by mRNA expression under a simple law of mass action based model.

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE72336 | GEO | 2017/04/30

SECONDARY ACCESSION(S): PRJNA294012

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Systematic identification of changes in the yeast protein interaction network in response to environmental, chemical, and genetic perturbation [transcriptome data]

Project description:To understand the principles underlying protein-protein interaction (PPI) complex changes in response to external perturbations, we created a highly multiplexed version of the murine dihydrofolate reductase protein complementation assay (mDHFR PCA) in Saccharomyces cerevisiae, allowing quantitative PPI complex profiling in vivo. We investigated the effects of 14 different conditions (including small molecules, abiotic stress factors, and nutrient composition) on a total of 1383 PPIs. More than half of PPIs (758) were found to be variable, and their Gene Ontology (GO) annotations were found to be informative of both the nature of the perturbation within each condition, as well as the overall variability of the interactions across conditions. Many perturbations triggered network changes characterized by large connected modules centered around highly connected proteins ('hubs'), suggesting that cellular control of a few proteins (e.g., by mRNA levels) can induce widespread PPI remodeling. Under a diauxic shift from glucose to ethanol as the main carbon source, we found a striking relationship between PPI changes measured by our assay and those predicted by mRNA expression under a simple law of mass action based model.

2017-04-30 | GSE72423 | GEO

Systematic identification of changes in the yeast protein interaction network in response to environmental, chemical, and genetic perturbation [transcriptome data]

2017-04-30 | E-GEOD-72423 | biostudies-arrayexpress

Expression data from young and senescent HCAECs treated with proton pump inhibitors (omeprazole and lansoprazole)

Project description:Proton pump inhibitors (PPIs) are among the most frequently prescribed drugs, especially in older people. Although these drugs are usually considered safe, recent evidence suggests that high dose and/or long term use of PPIs may have several detrimental effects, including increased risk of adverse cardiovascular events. The impact of PPI in the aging host environment still need to be characterized. Aged tissues, including vascular tissues, accumulate senescent cells that can communicate with their environment by secreting a myriad of cytokines and growth factors. Human coronary artery endothelial cells (HCAECs) provide an excellent model system to study â??in vitroâ?? most aspects of cardiovascular function and disease related to cellular senescence. The purpose of this study is thus to investigate the in vitro effects of two well-known PPIs (Omeprazole and Lansoprazole) on endothelial gene expression in senescent e non-senescent HCAECs. We used a cDNA microarray method to compare gene expression profiles of young and senescent HCAECs treated with omeprazole and lansoprazole. Young cells were cultured in medium supplemented with vehicle (CTRL) or 100Î¼M PPIs (Omeprazole or Lansoprazole) and grown for subsequent passages until they evidenced senescence-associated phenotypes. Total mRNA was extracted and gene expression profiles were analyzed in senescent endothelial cells (P12) compared to the non-senescent cells (P6) in both untreated (CTRL) and PPI-treated groups by cDNA microarray. All experiments were performed in triplicate for each treatment group.

2017-04-18 | E-GEOD-77239 | biostudies-arrayexpress

Transcriptome analysis of FKBP25 siRNA-knockdown in cultured HEK 293 cells

Project description:Peptidyl-prolyl isomerases (PPIs) are a superfamily of ubiquitous enzymes that catalyze the cis-trans interconversion of Xaa-Pro peptide bonds. The functions of most PPIs remain uncharacterized. FKBP25, a mammalian PPI, localizes to the nucleus, binds nucleic acids, and associates with chromatin modifying enzymes. However, the role of this protein in transcriptional regulation remains unclear. To help address this question we present RNA-seq gene expression profiling in HEK293 cells transfected with siRNA targeting FKBP25 relative to a GFP-targeting negative control.

2018-01-03 | GSE100813 | GEO

Molecular signaling distinguishes early from late recurrences in ER positive breast cancer

Project description:Unlike many other cancers, estrogen receptor-alpha (ER+) breast cancers are associated with cumulative risks of recurrence and death that persist for decades. We show that molecular differences between breast cancers that recur at distant sites early (² 3 years) or late (³ 5 years) support a robust molecular predictor of recurrence with Tamoxifen therapy and provide novel insights into the signaling features that differ between these recurrent cancers. We applied a support vector machine with recursive feature elimination to gene expression microarray data using a training-internal crossvalidation workflow that minimizes the gene selection bias problem. The resulting predictor was validated in an independent data set. Performance of the predictor suggests that it is possible to identify patients at increased risk of experiencing an early recurrence who require other treatments to prevent early metastasis. We implemented a Metropolis Sampling algorithm as a random walk to identify the protein-protein interactions (PPI) most closely associated with ER in 8 PPI databases. We then walked the gene expression data for the top PPIs to discover a signaling network driving early and late recurrent breast cancers. Consensus features between the training and validation datasets define a complex and highly connected network with major interactions among nodes including AR, CALD1, CALM(1,2,3), CDK1, EGFR, ESR1, ESR2, MAPK1, and SRC. The complexity illustrates the challenges in directing single agent or simple combination therapies to improve overall survival in ER+ breast cancers that will recur but also suggests potentially novel interventions to address this challenge. 49 independent samples (breast tumors) were arrayed

2015-04-15 | E-GEOD-46222 | biostudies-arrayexpress

Expression data from young and senescent HCAECs treated with proton pump inhibitors (omeprazole and lansoprazole)

2017-04-18 | GSE77239 | GEO

Multiplexed profiling of kinase interactomes quantifies cellular network plasticity

Project description:Cancers rewire protein-protein interaction (PPI) networks to promote disease progression, making PPIs attractive drug targets. Kinases are key druggable nodes in PPI networks but high-throughput proteomics approaches to map their interactomes are lacking. We introduce kinobead competition and correlation analysis (Ki-CCA), a chemoproteomics approach combining affinity enrichment of kinases and their interaction partners with broad-selectivity binding competition to map hundreds of endogenous kinase PPIs simultaneously. We identified 2,305 PPIs of 300 kinases across 18 diverse cancer lines, demonstrating that kinase interaction networks show high plasticity between cancer types, signaling, and phenotypic states. We discovered an AAK1 complex promoting epithelial-mesenchymal transition and drug resistance, and that genetic knockdown of AAK1 complex components sensitizes cells to targeted therapy. Ki-CCA enables rapid and highly multiplexed mapping of kinome PPIs in native cell and tissue lysates, not requiring epitope tagged baits or antibodies. Our PPI database presents an important resource for cancer research.

| MSV000088067 | MassIVE

Integrative Multi‑omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma

Project description:We analyzed differentially expressed genes (DEGs) associated with OSCC using RNA sequencing technology. Methylation‑regulated and differentially expressed genes (MeDEGs) of OSCC were further identified via an integrative approach by examining methylomic datasets together with our own transcriptomic data. Protein‑protein interaction (PPI) networks of MeDEGs were constructed and highly connected hub MeDEGs were identified from these PPI networks. A total of 56 upregulated MeDEGs and 170 downregulated MeDEGs were identified in OSCC. Eleven hub genes with high degree of connectivity were picked out from the protein‑protein interaction (PPI) networks constructed by those MeDEGs.

2022-10-31 | GSE186775 | GEO

Molecular signaling distinguishes early from late recurrences in ER positive breast cancer

2015-04-15 | GSE46222 | GEO

Proximity-dependent biotinylation by TurboID to identify protein-protein interaction networks in yeast

Project description:The use of proximity-dependent biotinylation assays coupled to mass spectrometry (PDB-MS) has changed the field of protein-protein interactions (PPI) studies. Yet, despite the recurrent and successful use of BioID-based PPI screening in mammalian cells, the implementation of PDB-MS in yeast has not been effective. Here we report a simple and rapid approach in yeast to effectively screen for proximal and interacting proteins in their natural cellular environment by using TurboID, a recently described version of the BirA biotin ligase. Using the protein arginine methyltransferase Rmt3 and the RNA exosome subunits, Rrp6 and Dis3, the application of PDB-MS in yeast by using TurboID was able to recover protein-protein interactions previously identified using other biochemical approaches and provided complementary information for a given protein bait. The development of a rapid and effective PDB assay that can systematically analyze PPIs in living yeast cells opens the way for large-scale proteomics studies in this powerful model organism.

2019-05-16 | PXD013183 | Pride

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