Hepatocellular adenomas (HCAs) are benign liver tumors that usually develop in women who are taking oral contraceptives. Among these tumors, biallelic inactivating mutations of the hepatocyte nuclear factor 1␣ (HNF1A) transcription factor have been frequently identified and in rare cases of hepatocellular carcinomas developed in noncirrhotic liver. Because HNF1A meets the genetic criteria of a tumor suppressor gene, we aimed to elucidate the tumorigenic mechanisms related to HNF1␣ inactivation in hepatocytes. We searched for signaling pathways aberrantly activated in human HNF1A-mutated HCA (H-HCA) using a genome-wide transcriptome analysis comparing five H-HCA with four normal livers. We validated the main pathways by quantitative reverse transcription polymerase chain reaction (RT-PCR) and western blotting in a large series of samples. Then, we assessed the role of HNF1␣ in the observed deregulations in hepatocellular cell models (HepG2 and Hep3B) by silencing its endogenous expression using small interfering RNA. Along with the previously described induction of glycolysis and lipogenesis, H-HCA also displayed overexpression of several genes encoding growth factor receptors, components of the translation machinery, cell cycle, and angiogenesis regulators, with, in particular, activation of the mammalian target of rapamycin (mTOR) pathway. Moreover, estradiol detoxification activities were shut down, suggesting a hypersensitivity of H-HCA to estrogenic stimulation. In the cell model, inhibition of HNF1␣ recapitulated most of these identified transcriptional deregulations, demonstrating that they were related to HNF1␣ inhibition. Conclusion: H-HCA showed a combination of alterations related to HNF1␣ inactivation that may cooperate to promote tumor development. Interestingly, mTOR appears as a potential new attractive therapeutic target for treatment of this group of HCAs. (

The rumen microbiota is an essential part of ruminants shaping their nutrition and health. Despite its importance, it is not fully understood how various groups of rumen microbes affect host-microbe relationships and functions. The aim of the study was to simultaneously explore the rumen microbiota and the metabolic phenotype of lambs for identifying host-microbe associations and potential biomarkers of digestive functions. Twin lambs, separated in two groups after birth were exposed to practices (isolation and gavage with rumen fluid with protozoa or protozoa-depleted) that differentially restricted the acquisition of microbes. Rumen microbiota, fermentation parameters, digestibility and growth were monitored for up to 31 weeks of age. Microbiota assembled in isolation from other ruminants lacked protozoa and had low bacterial and archaeal diversity whereas digestibility was not affected. Exposure to adult sheep microbiota increased bacterial and archaeal diversity independently of protozoa presence. For archaea, Methanomassiliicoccales displaced Methanosphaera. Notwithstanding, protozoa induced differences in functional traits such as digestibility and significantly shaped bacterial community structure, notably Ruminococcaceae and Lachnospiraceae lower up to 6 folds, Prevotellaceae lower by ~40%, and Clostridiaceae and Veillonellaceae higher up to 10 folds compared to microbiota without protozoa. An orthogonal partial least squares-discriminant analysis of urinary metabolome matched differences in microbiota structure. Discriminant metabolites were mainly involved in amino acids and protein metabolic pathways while a negative interaction was observed between methylotrophic methanogens Methanomassiliicoccales and trimethylamine N-oxide. These results stress the influence of gut microbes on animal phenotype and show the potential of metabolomics for monitoring rumen microbial functions.

High resolution mass spectrometry (HRMS) is increasingly used to produce metabolomics data. Thanks to its high mass resolution and mass measurement accuracy, it is also very useful for metabolite identification. Nevertheless, a rigorous methodology is required. This manuscript describes different steps involved in the structural elucidation of metabolites and demonstrates the utility of HRMS for such purpose. After a brief overview of HRMS performances in terms of mass measurement accuracy, peak resolution, isotopic clusters/patterns and the instrumentation used, the first section is devoted to the data processing generally performed to reduce the data set size. Based on the mass accuracy measurements, different post-acquisition data processing procedures have been developed for complex mixture analysis and can be used in metabolomics. The second section describes protocols used to process putative metabolite annotations or identifications with HRMS data, based on elemental composition determined from accurately measured m/z value and mass spectral databases. Non-classical approaches are also proposed for tentative structure elucidation of unknown metabolites. Finally, limitations of the proposed workflow for metabolite structure elucidation are discussed and possible improvements are proposed