RT Journal Article
SR Electronic
T1 Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks
JF Science Signaling
JO Sci. Signal.
FD American Association for the Advancement of Science
SP eaaq1087
DO 10.1126/scisignal.aaq1087
VO 11
IS 531
A1 Grimes, Mark
A1 Hall, Benjamin
A1 Foltz, Lauren
A1 Levy, Tyler
A1 Rikova, Klarisa
A1 Gaiser, Jeremiah
A1 Cook, William
A1 Smirnova, Ekaterina
A1 Wheeler, Travis
A1 Clark, Neil R.
A1 Lachmann, Alexander
A1 Zhang, Bin
A1 Hornbeck, Peter
A1 Ma’ayan, Avi
A1 Comb, Michael
YR 2018
UL http://stke.sciencemag.org/content/11/531/eaaq1087.abstract
AB Various “omics” analyses of cells provide insight into cellular health and behavior. Phosphoproteomic analysis has informed much of our current knowledge of cell signaling networks and facilitated drug development. Less appreciated are the inputs of other posttranslational modifications (PTMs). Grimes et al. integrated genomic, proteomic, phosphoproteomic, acetylomic, and methylomic data from lung cancer cells versus those in normal lung tissue and explored various regulatory patterns. The authors found that many PTMs are exclusive, in that as phosphorylation increased, acetylation decreased; that the drug geldanamycin broadly alters the PTM landscape and, thus, has effects far beyond its target; and that RNA binding proteins appear to be critical effectors of many signaling paths. These networks may inform new drug development for lung cancer patients and exemplify how cell signaling is regulated by far more extensive PTM networks than was previously appreciated.Protein posttranslational modifications (PTMs) have typically been studied independently, yet many proteins are modified by more than one PTM type, and cell signaling pathways somehow integrate this information. We coupled immunoprecipitation using PTM-specific antibodies with tandem mass tag (TMT) mass spectrometry to simultaneously examine phosphorylation, methylation, and acetylation in 45 lung cancer cell lines compared to normal lung tissue and to cell lines treated with anticancer drugs. This simultaneous, large-scale, integrative analysis of these PTMs using a cluster-filtered network (CFN) approach revealed that cell signaling pathways were outlined by clustering patterns in PTMs. We used the t-distributed stochastic neighbor embedding (t-SNE) method to identify PTM clusters and then integrated each with known protein-protein interactions (PPIs) to elucidate functional cell signaling pathways. The CFN identified known and previously unknown cell signaling pathways in lung cancer cells that were not present in normal lung epithelial tissue. In various proteins modified by more than one type of PTM, the incidence of those PTMs exhibited inverse relationships, suggesting that molecular exclusive “OR” gates determine a large number of signal transduction events. We also showed that the acetyltransferase EP300 appears to be a hub in the network of pathways involving different PTMs. In addition, the data shed light on the mechanism of action of geldanamycin, an HSP90 inhibitor. Together, the findings reveal that cell signaling pathways mediated by acetylation, methylation, and phosphorylation regulate the cytoskeleton, membrane traffic, and RNA binding protein–mediated control of gene expression.