Abstract | In a phosphoproteomic study of neuroblastoma cell lines and cell fractions, including endo-somes and detergent-resistant membranes, 1622 phosphorylated proteins were detected, including more than half ofthe receptortyrosine kinases in the human genome. |
Introduction | Neuroblastoma tumors and cell lines thus represent a snapshot of failed differentiation at different stages in the neural crest sympathoa-drenal lineage [2,4,7,8]. |
Introduction | To identify patterns in tyrosine phosphorylation in neuroblastoma, we acquired phospho-proteomic data from 21 neuroblastoma cell lines and cell fractions including endosomes and detergent-resistant lipid rafts as previously characterized [32,33]. |
Neuroblastoma Phosphoproteomic Network | These data indicate that neuroblastoma cell lines express and phosphorylate a large fraction of the PNCPs in the human genome. |
Neuroblastoma Phosphoproteomic Network | Notably, four different transplanted human neuroblastoma cell lines [LAN6, SK-N-BE(2), SMS-KCN, and SH-SY5Y] migrated to neural crest target sites, incorporated into the developing ganglia, and expressed neuronal markers specific to mature afferents (S4 Fig). |
Neuroblastoma Phosphoproteomic Network | The potential to migrate along the stereotypical neural crest migration pathways, and differentiate into most neural-crest-derived cell types, suggests that many of the RTK signaling pathways that control differentiation and migration were generally functional in these neuroblastoma cell lines . |
Phosphoproteomics | To identify patterns in tyrosine phosphorylation in neuroblastoma, we analyzed tyrosine phos-phoproteomic data acquired from 21 neuroblastoma cell lines using immunoprecipitation of tyrosine phosphorylated peptides as previously described [41,42]. |
Phosphoproteomics | Four cell lines [SH-SY5Y, LAN-6, SMS-KCN, and SK-N-BE(2)] were selected for further studies because of their different point mutations in ALK, p53 status, RTK expression, morphology, and growth patterns. |
Tyrosine Kinase Posphorylation in Response to RTK Stimulation | These data indicate that stimulation of one RTK affects the phosphorylation state of other RTKs in neuroblastoma cell lines . |
Tyrosine Kinase Posphorylation in Response to RTK Stimulation | Shown are changes of more than twofold from representative experiments where peak intensity was measured for treatment and control conditions in the same experiment with cell lines and treatments indicated on column labels (e.g., “NGF to C” means NGF-treated compared to control). |
Tyrosine Kinase Posphorylation in Response to RTK Stimulation | In contrast, FYN inhibitory phosphorylation (FYN 531) was increased by NGF in two cell lines (Fig 4B). |
Discussion | By using two different glioblastoma cell lines in our analysis, we have found that glioblasto-ma growth through the insulin signaling pathway is tumor specific. |
Discussion | When we conducted the glioblastoma growth reduction analyses of the LN229 and U87 cell lines, there was almost no change in growth observed in the U87 cell lines , while the LN229 showed a reduction in the glioblastoma tumors’ growth. |
Discussion | Glioblastoma cells lines that rely on the insulin signaling pathway for their aggressive growth phenotype will be more affected by drugs that target the insulin signaling pathway. |
Fitting model parameters | The glioblastoma growth rates were found for two distinct experiments (U87 and LN229) by fitting the same model and obtaining different initial conditions and growth rates for the two cell lines . |
Fitting model parameters | The experiments used the human retinal pigment epithelial (RPE) cell line D407; and it is an assumption of the model that the same relationships hold in glioma cells (these measurements are the only ones we are aware of that measure IGFBP2 as a function of IGFI levels). |
Glioblastoma growth reduction | The diameter of the glioblastoma for both cell lines U87 and LN229 was then compared to the original pathway before the removal of the reaction. |
Growth of glioblastoma experiments | The growth rate of the glioblastoma tumor, Eq 5, was determined by regression analysis using the data from both our previous experiments on spheroid growth in vitro using the U87 glio-blastoma cell line and LN229 glioblastoma growth in mice [70]. |
Growth of glioblastoma experiments | The U87 and LN229 glioblas-toma cell lines were used to compare glioblastoma cell lines which were more dependent on insulin signaling (LN229) and less dependent on insulin signaling (U87) [3]. |
Insulin signaling pathway reactions that drive glioma growth | for the U87 cell line , there was not a significant change in the glioblastoma volume When either the IGFBP2 to HIFloc or the IGFI to HIFloc connection was removed, see SS Fig. |
Sensitivity analysis | Sensitivity analysis was summarized by calculating the sensitivity indeX (see below) at 40 days for the LN229 cell line in Table 1. |
Abstract | This power-law captures the complex, variable processes underlying bacterial invasion while also enabling differentiation of cell lines . |
Author Summary | The power-law parameters capture characteristics of the host-bacterium pair interaction and can differentiate host cell lines . |
Characteristic power-law parameters describe uptake in different cell lines | Characteristic power-law parameters describe uptake in different cell lines |
Characteristic power-law parameters describe uptake in different cell lines | To test this hypothesis, we measured the uptake dynamics of our engineered bacteria (E. coli expressing arabinose-inducible invasin) in several mammalian cell lines , grown under the same condition. |
Discussion | Though simple, the power-law description of uptake is robust in describing invasin-mediated bacterial uptake by mammalian cells, and the extracted power-law parameters can be useful in distinguishing pairs of E. coli strains (expressing varying levels of invasion) and mammalian cell lines . |
Log 1IKM | In particular, we treated several cell lines with increasing MOI. |
Log 1IKM | As such, for more extensive analysis of different cell lines , we chose a high MOI for bacterial infection. |
Log 1IKM | Results in Fig 4B reveal that power-law parameters [3 and 1/ KDeff for different cell lines infected with bacteria at 1000 MOI appeared to fall along a single trajectory. |
Variability in invasin-mediated bacterial uptake | Uptake in all the cell lines we tested, most of which are cancer models, was positively correlated with MOI but the amount of uptake was quite variable. |
Abstract | We also explore the function of non-coding RNAs in the attenuation of the immediate early response in a small RNA sequencing dataset matched to the CAGE data: We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line . |
Author Summary | We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line . |
Introduction | The activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) in the MCF7 breast cancer cell line exemplifies the impact of such transient or sustained signalling on cell fate [3, 4]. |
Kinetics and chromatin features underlying IEG induction | This pattern was less apparent in the MCF7-HRG cell line where the proportion of known IEGs found in an interval exceeded the overall average towards the end of the time course. |
Applications | We extracted gene regulatory network data from the ENCODE leukemia cell line , K562, and gene and miRNA eXpression datasets for AML from TCGA. |
Gene expression, transcription factor and miRNA datasets | We identified 50,865 TF1-TF2-target triplets with 1824 unique targets using ChIP-seq data (70 TFs) from ENCODE K562 cell line [5] and 821 distTF-TF-target triplets with 113 unique targets, where distTFs were predicted to bind distal regulatory regions [52]. |
Gene expression, transcription factor and miRNA datasets | The miRNA-target pairs that we used for human K562 cell line in this paper were the overlapped pairs among widely used public databases for predicting miRNA-target relationships described in [53]. |