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Alexopoulos et al. 2010

Networks Inferred from Biochemical Data Reveal Profound Differences in Toll-like Receptor and Inflammatory Signaling between Normal and Transformed Hepatocytes
Alexopoulos, L.G., Saez-Rodriguez, J., Cosgrove, B.D., Lauffenburger, D.A., and Sorger, P.K. (2010). Molecular & Cellular Proteomics 9, 1849-1865.

Systematic study of cell signaling networks increasingly involves high throughput proteomics, transcriptional profiling, and automated literature mining with the aim of assembling large scale interaction networks. In contrast, functional analysis of cell signaling usually focuses on a much smaller sets of proteins and eschews computation but focuses directly on cellular responses to environment and perturbation. We sought to combine these two traditions by collecting cell response measures on a reasonably large scale and then attempting to infer differences in network topology between two cell types. Human hepatocytes and hepatocellular carcinoma cell lines were exposed to inducers of inflammation, innate immunity, and proliferation in the presence and absence of small molecule drugs, and multiplex biochemical measurement was then performed on intra- and extracellular signaling molecules. We uncovered major differences between primary and transformed hepatocytes with respect to the engagement of toll-like receptor and NF-kappaB-dependent secretion of chemokines and cytokines that prime and attract immune cells. Overall, our results serve as a proof of principle for an approach to network analysis that is systematic, comparative, and biochemically focused. More specifically, our data support the hypothesis that hepatocellular carcinoma cells down-regulate normal inflammatory and immune responses to avoid immune editing.

A small subset of the data set described in this paper has been used to train a Boolean model with the tool CellNetOptimizer.

The data describes the phosphorylation of proteins in HepG2 cells 30 minutes after stimulation with combinations of 7 ligands and 7 inhibitors. This subset of the data can be loaded here in MIDAS format that can be loaded, visualized, and processed with DataRail.

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