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Identification of transcriptional networks involved in peroxisome proliferator chemical-induced hepatocyte proliferation
Citation:
VALLANAT, B., R. Currie, J. R. PIRONE, A. V. Singh, F. Elloumi, C. CORTON, AND I. A. SHAH. Identification of transcriptional networks involved in peroxisome proliferator chemical-induced hepatocyte proliferation. Presented at Society of Toxicology Annual Meeting, Baltimore, MD, March 15 - 19, 2009.
Impact/Purpose:
This suggests sequential non-overlapping regulatory events controlling cell cycle gene expression. Putative transcription factor binding sites were predicted in the 10 Kb 5' upstream region of DEG's at each time point by sequence analysis (using the TRANSFAC® 2008.2 database). Statistically significant transcriptional sub networks were inferred computationally from putative genetic-regulatory interactions. The putative transcription networks of regulated cell cycle genes were compared with published data. This work identified a number of genes that may underlie the increases in cell proliferation observed upon PPC exposure.
Description:
Peroxisome proliferator chemical (PPC) exposure leads to increases in rodent liver tumors through a non-genotoxic mode of action (MOA). The PPC MOA includes increased oxidative stress, hepatocyte proliferation and decreased apoptosis. We investigated the putative genetic regulatory events leading to hepatocellular proliferation following short-term exposure to the PPC di-2-ethylhexyl phthalate (DEHP). Male B6C3F1 mice were exposed to DEHP via oral gavage at a single concentration of 1150 mg/kg/day and sacrificed after 2, 8, 24 and 72 hours. Liver samples were analyzed using the Affymetrix Mouse 430_2 genechip and had identified 794 (2hr), 1921 (8hr), 2019 (24hr) and 2076 (72hr) transcript clusters to be statistically significant. Cell proliferation was markedly increased at 72 hrs. Analyzing differentially expressed genes (DEGs) in the context of cell proliferation showed roughly 10% be related to cell cycle at each time point with very little overlap across the time points.