Dataset Information

Large-scale phosphorylation site interaction proteomics

ABSTRACT: Receptor tyrosine kinases (RTKs) are membrane proteins that regulate complex, multilayered signaling networks centered on tyrosine phosphorylation of proteins with broad implications in human health and disease. Here we developed a large-scale mass spectrometry-based proteomics platform for investigation of in-vivo RTK signaling networks and the dynamic protein interactions they induce.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Rattus Norvegicus (rat)

TISSUE(S): Lung

SUBMITTER: Christian Kelstrup

LAB HEAD: Jesper V. Olsen

PROVIDER: PXD004055 | Pride | 2019-10-04

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20130325_QE5_LC4_ALL_SA_Rat_Lung_pTyr_1.raw	Raw
	20130325_QE5_LC4_ALL_SA_Rat_Lung_pTyr_10.raw	Raw
	20130325_QE5_LC4_ALL_SA_Rat_Lung_pTyr_11.raw	Raw
	20130325_QE5_LC4_ALL_SA_Rat_Lung_pTyr_12.raw	Raw
	20130325_QE5_LC4_ALL_SA_Rat_Lung_pTyr_13.raw	Raw

Items per page:

1 - 5 of 347

Publications

Oncogenic Mutations Rewire Signaling Pathways by Switching Protein Recruitment to Phosphotyrosine Sites.

Lundby Alicia A Franciosa Giulia G Emdal Kristina B KB Refsgaard Jan C JC Gnosa Sebastian P SP Bekker-Jensen Dorte B DB Secher Anna A Maurya Svetlana R SR Paul Indranil I Mendez Blanca L BL Kelstrup Christian D CD Francavilla Chiara C Kveiborg Marie M Montoya Guillermo G Jensen Lars J LJ Olsen Jesper V JV

Cell 20191001 2

Tyrosine phosphorylation regulates multi-layered signaling networks with broad implications in (patho)physiology, but high-throughput methods for functional annotation of phosphotyrosine sites are lacking. To decipher phosphotyrosine signaling directly in tissue samples, we developed a mass-spectrometry-based interaction proteomics approach. We measured the in vivo EGF-dependent signaling network in lung tissue quantifying >1,000 phosphotyrosine sites. To assign function to all EGF-regulated sit ...[more]

PMID: 31585087

Similar Datasets

Project description:Background: Signaling by receptor tyrosine kinases (RTK) is frequently dysregulated in gliomas. Inter-individual variability in the causes for dysregulated RTK signaling may have hampered the efficacy of targeted therapies. Using gene expression modules around key regulators in the RAS-RAF-MEK-MAPK cascade and in the phosphatidylinositol 3-kinase-AKT pathways, we developed a âRMPAâ clustering scheme to distinguish gliomas with varying extents of RTK signaling. Results: We identified gene modules consistently co-expressed with NF1 (NF1-M), Sprouty (SPRY-M) and PTEN (PTEN-M) in gliomas. Their signatures enabled robust clustering of adult diffuse gliomas of WHO grades II-IV into RMPAhigh and RMPAlow phenotypes in a morphology-independent manner. In five independent data sets from three continents containing more than 1500 adult diffuse gliomas, RMPAhigh gliomas were associated with poor prognosis while RMPAlow gliomas were not. The RMPAhigh and RMPAlow glioma subtypes showed distinct levels of the activities of RAS-RAF-MEK-MAPK cascade and PI3K-AKT pathway and harbored unique sets of genomic alterations in the RTK signaling-related genes. The RMPAhigh gliomas contained large numbers of immature vessel cells and tumor associated macrophages and both cell types expressed high levels of pro-angiogenic RTKs including MET, VEGFR1, KDR, EPHB4 and NRP1. Conclusion: Inter-glioma variability in RTK signaling activities can be defined using the RMPA clustering scheme. The combined signatures of NF1-M, SPRY-M and PTEN-M reflect RTK signaling activity both in the glioma cells and in the glioma microenvironment. Our data show that RTK signaling in the glioma microenvironment may play a pivotal role in glioma progression. Transcriptome data from 22 fresh gliomas (2 astrocytoma II, 1 oligodendrocytoma II, 7 oligoastrocytoma II, 4 anaplastic oligoastrocytoma III and 8 GBM) were obtained using Affymetrix Human Gene 1.0 ST Array. Unsupervised hierarchical clustering of the expression data for the SPRY-M, NF1-M and PTEN-M was performed on these transcriptome data to identify samples with RMPAhigh or RMPAlow signature.