Networks Inferred from Biochemical Data Reveal Profound Differences in Toll-like Receptor and Inflammatory Signaling between Normal and Transformed Hepatocytes

• Main Authors: Alexopoulos, Leonidas G. (Author), Rodriguez, Julio Saez (Author), Cosgrove, Benjamin D. (Contributor), Lauffenburger, Douglas A. (Contributor), Sorger, Peter K. (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering (Contributor)
• Format: Article
• Language: English
• Published: American Society for Biochemistry and Molecular Biology (ASBMB), 2013-01-09T21:29:51Z.
• Subjects: Article
• Online Access: Get fulltext

 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 cellresponse measures on a reasonably large scale and then attempting to infer differences in network topology between two cell types. Human hepatocytes and hepatocellular carcinoma (HCC) 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 then performed on intra- and extracellular signaling molecules. We uncover major differences between primary and transformed hepatocytes with respect to the engagement of toll-like receptor and NF-κBdependent 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 HCC cells down-regulate normal inflammatory and immune responses to avoid immune editing.