System analysis of cross-talk between nuclear receptors reveals an opposite regulation of the cell cycle by LXR and FXR in human HepaRG liver cells

Abstract

Transcriptional regulations exert a critical control of metabolic homeostasis. In particular, the nuclear receptors (NRs) are involved in regulating numerous pathways of the intermediate metabolism. The purpose of the present study was to explore in liver cells the interconnectedness between three of them, LXR, FXR, and PPARα, all three known to act on lipid and glucose metabolism, and also on inflammation. The human cell line HepaRG was selected for its best proximity to human primary hepatocytes. Global gene expression of differentiated HepaRG cells was assessed after 4 hours and 24 hours of exposure to GW3965 (LXR agonist), GW7647 (PPARα agonist), and GW4064 and CDCA (FXR synthetic and natural agonist, respectively). Our work revealed that, contrary to our expectations, NR specificity is largely present at the level of target genes, with a smaller than expected overlap of the set of genes targeted by the different NRs. It also highlighted the much broader activity of the synthetic FXR ligand compared to CDCA. More importantly, our results revealed that activation of FXR has a pro-proliferative effect and decreases polyploidy of hepatocytes, while LXR inhibits the cell cycle progression, inducing hepatocyte differentiation and a higher polyploidism. Conclusion: these results highlight the importance of analyzing the different NR activities in a context allowing a direct confrontation of each receptor outcome, and reveals the opposite role of FXR and LXR in hepatocyte cells division and maturation. Gene Set Enrichment Analysis (GSEA)Gene set enrichment analysis (GSEA) was performed as described in (9) for each treatment at each time point. Most of the gene sets were taken from KEGG, while BIOCARTA sets from the Broad Institute's Molecular Signatures Database (MsigDB version 3.0: www.broadinstitute.org/gsea/msigdb) are explicitly mentioned. Genes that were part of a gene set but that were not found on the Affymetrix chip via their gene symbol were removed from the gene sets. Of these reduced gene sets, those that had fewer than 10 or more than 500 genes were discarded, leaving a total of 327 gene sets. The gene sets were scored against the ranked lists from the LIMMA analysis (four per time point). The absolute t-values from the moderated t-tests were used for both ranking and weighting the genes. P-values were computed using sample permutation with 500 iterations, separately for each of the two time points. At each iteration, the limma model with four contrasts described in the section above was run on the permuted data in order to compute gene set scores for all four treatmentcontrol comparisons. The estimated p-value for a gene set is the proportion of sample permutations that led to a higher score than the original data. To correct for multiple testing, the p-values of the gene sets were adjusted by the Benjamini-Hochberg method. This was done globally for the four gene set lists from time point 4 hours and for the four gene-set lists from time point 24h. We considered a gene set potentially enriched if it ha...

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