ABSTRACT: Biallelic inactivating mutations of the transcription factor 1 gene (TCF1), encoding hepatocyte nuclear factor 1a (HNF1a), were identified in 50% of hepatocellular adenomas (HCA) phenotypically characterized by a striking steatosis. To understand the molecular basis of this aberrant lipid storage, we performed a microarray transcriptome analysis validated by quantitative RT-PCR, western-blotting and lipid profiling. In mutated HCA, we showed a repression of gluconeogenesis coordinated with an activation of glycolysis, citrate shuttle and fatty acid synthesis predicting elevated rates of lipogenesis. Moreover, the strong dowregulation of L-FABP suggests that impaired fatty acid trafficking may also contribute to the fatty phenotype. In addition, transcriptional profile analysis of the observed deregulated genes in non-HNF1a-mutated HCA as well as in non-tumor livers allowed us to define a specific signature of the HNF1a-mutated HCA. In theses tumors, lipid composition was dramatically modified according to the transcriptional deregulations identified in the fatty acid synthetic pathway. Surprisingly, lipogenesis activation did not operate through SREBP-1 and ChREBP that were repressed. We conclude that steatosis in HNF1a-mutated HCA results mainly from an aberrant promotion of lipogenesis that is linked to HNF1a inactivation and that is independent of both SREBP-1 and ChREBP activation. Finally, our findings have potential clinical implications since lipogenesis can be efficiently inhibited by targeted therapies. Keywords: Disease state analysis In a first experiment, microarray analyses were applied to 8 HNF1-alpha mutated HCA and their corresponding non-tumor livers using cDNA in-house manufactured arrays following a randomized and blinded unbalanced design. RNA labelling, hybridization and analysis of fluorescence was carried out, as described in [Graudens et al., Genome Biol 2006, 7: R19; PMID: 16542501], considering that uneven numbers of samples were randomly allocated to each of the engineers who were not aware of sample phenotypes. To assess data reproducibility and minimize dye bias effects, each of the samples was measured four times, twice with Cy3 and twice with Cy5. To ensure robustness and flexibility in data analysis, a reference design was used with a universal reference sample (Stratagene, USA) serving as a baseline for the comparisons of tumor samples. Hybridizations were performed onto an 11K human array (11K_VJF-ARRAY, GPL3282), which provides a genome-wide coverage of functional pathways. Raw data were obtained using the ArrayVisionM-bM-^DM-" 7.0 software (Imaging Research Inc., USA); the resulting 3.2x106 hybridization data points collected from 32 arrays were stored in a MIAME-compliant database and pre-processed for normalization and filtering as described in [Graudens et al., Genome Biol 2006, 7: R19; PMID: 16542501]. In a second experiment, HG-U133A Affymetrix GeneChipTM arrays were used to compare the expression profiles of 5 HNF1-alpha mutated HCA and 4 non related non-tumor livers. RNA labelling, hybridization and analysis were carried out following the manufacturerM-bM-^@M-^Ys instructions(Affymetrix, Santa Clara, CA). Raw data were obtained by using Microarray Suite 5.0 (MAS5) software, embedded in the Affymetrix GeneChip Operating Software (Santa Clara, USA); the resulting raw numerical data (CEL files) - available as supplementary files - collected from 9 Affymetrix GeneChips were pre-processed for normalization and filtering as described in [Rebouissou et al., J Biol Chem 2007, in press; PMID: 17379603]. A platform-dependant statistical comparison was performed considering HNF1 alpha-mutated HCA versus non tumor liver tissues, using multiple testing procedures to evaluate statistical significance for differentially expressed genes, as described in [Graudens et al., Genome Biol 2006, 7: R19; PMID: 16542501] and in the supplemental experimental procedures in [Rebouissou et al., J Biol Chem 2007, in press; PMID: 17379603]. Thus, a combined cross-platform analysis was done considering the UGCluster Identifiants as a common primary key (Unigene Build184-June05). Additional information on the procedures is provided in the supplemental experimental procedures in [Rebouissou et al., J Biol Chem 2007, in press; PMID: 17379603].
