Fuzzy modeling reveals a dynamic self-sustaining network of the GLI transcription factors controlling important metabolic regulators in adult mouse hepatocytes.

The GLI transcription factors, GLI1, GLI2, and GLI3, transduce Hedgehog and non-hedgehog signals and are involved in regulating development and tumorgenesis. Surprisingly, they were recently found to modulate important functions of mature liver. However, less is known about their mutual interactions and possible target genes in mature hepatocytes. To get a deeper insight into these interactions cultured mouse hepatocytes were transfected with siRNAs against each GLI factor. RNA was extracted at different times and the expression levels of the genes of interest were determined by quantitative real-time PCR. The time-dependent data were analysed by a fuzzy logic-based modelling approach. The results indicated that the GLI factors constitute an interconnected network. GLI2 inhibited GLI1 expression and was coupled with GLI3 by a positive feedback loop. The regulatory activity between GLI1 and GLI3 was more complex switching between a positive and a negative feedback loop depending on whether the level of GLI2 is low or high, respectively. Generally, this network structure enables a dynamic behaviour. When GLI2 is low, it may keep GLI1 and GLI3 activity balanced favouring the appropriate modulation of transcription factors like the Ppars and Srebp1. When GLI2 is high, it may prevent an uncontrolled amplification that may lead to cancer. In conclusion, the three GLI factors in mature hepatocytes form an interactive transcriptional network that is involved in the control of target genes associated with metabolic zonation as well as with lipid and drug metabolism. Its structure in mature cells seems different from embryonic cells.

Hepatic Hedgehog signaling contributes to the regulation of IGF1 and IGFBP1 serum levels.

BACKGROUND: Hedgehog signaling plays an important role in embryonic development, organogenesis and cancer. In the adult liver, Hedgehog signaling in non-parenchymal cells has been found to play a role in certain disease states such as fibrosis and cirrhosis. However, whether the Hedgehog pathway is active in mature healthy hepatocytes and is of significance to liver function are controversial. FINDINGS: Two types of mice with distinct conditional hepatic deletion of the Smoothened gene, an essential co-receptor protein of the Hedgehog pathway, were generated for investigating the role of Hedgehog signaling in mature hepatocytes. The knockout animals (KO) were inconspicuous and healthy with no changes in serum transaminases, but showed a slower weight gain. The liver was smaller, but presented a normal architecture and cellular composition. By quantitative RT-PCR the downregulation of the expression of Indian hedgehog (Ihh) and the Gli3 transcription factor could be demonstrated in healthy mature hepatocytes from these mice, whereas Patched1 was upregulated. Strong alterations in gene expression were also observed for the IGF axis. While expression of Igf1 was downregulated, that of Igfbp1 was upregulated in the livers of both genders. Corresponding changes in the serum levels of both proteins could be detected by ELISA. By activating and inhibiting the transcriptional output of Hedgehog signaling in cultured hepatocytes through siRNAs against Ptch1 and Gli3, respectively, in combination with a ChIP assay evidence was collected indicating that Igf1 expression is directly dependent on the activator function of Gli3. In contrast, the mRNA level of Igfbp1 appears to be controlled through the repressor function of Gli3, while that of Igfbp2 and Igfbp3 did not change. Interestingly, body weight of the transgenic mice correlated well with IGF-I levels in both genders and also with IGFBP-1 levels in females, whereas it did not correlate with serum growth hormone levels. CONCLUSIONS: Our results demonstrate for the first time that Hedgehog signaling is active in healthy mature mouse hepatocytes and that it has considerable importance for IGF-I homeostasis in the circulation. These findings may have various implications for mouse physiology including the regulation of body weight and size, glucose homeostasis and reproductive capacity.

The extended TILAR approach: a novel tool for dynamic modeling of the transcription factor network regulating the adaption to in vitro cultivation of murine hepatocytes.

BACKGROUND: Network inference is an important tool to reveal the underlying interactions of biological systems. In the liver, a complex system of transcription factors is active to distribute signals and induce the cellular response following extracellular stimuli. Plenty of information is available about single transcription factors important for the different functions of the liver, but little is known about their causal relations to each other. RESULTS: Given a DNA microarray time series dataset of collagen monolayers cultured murine hepatocytes, we identified 22 differentially expressed genes for which the corresponding protein is known to exhibit transcription factor activity. We developed the Extended TILAR (ExTILAR) network inference algorithm based on the modeling concept of the previously published TILAR algorithm. Using ExTILAR, we inferred a transcription factor network based on gene expression data which puts these important genes into a functional context. This way, we identified a previously unknown relationship between Tgif1 and Atf3 which we validated experimentally. Beside its known role in metabolic processes, this extends the knowledge about Tgif1 in hepatocytes towards a possible influence of processes such as proliferation and cell cycle. Moreover, two positive (i.e. double negative) regulatory loops were predicted that could give rise to bistable behavior. We further evaluated the performance of ExTILAR by systematic inference of an in silico network. CONCLUSIONS: We present the ExTILAR algorithm, which combines the advantages of the regression based inference algorithm TILAR, like large network sizes processable and low computational costs, with the advantages of dynamic network models based on ordinary differential equation (i.e. in silico knock-down simulations). Like TILAR, ExTILAR makes use of various prior-knowledge types such as transcription factor binding site information and gene interaction knowledge to infer biologically meaningful gene regulatory networks. Therefore, ExTILAR is especially useful when a large number of genes is modeled using a small number of experimental data points.