Nonalcoholic fatty liver disease (NAFLD) can progress from simple steatosis (i.e., nonalcoholic fatty liver [NAFL]) to nonalcoholic steatohepatitis (NASH), cirrhosis, and cancer. Currently, the driver for this progression is not fully understood; in particular, it is not known how NAFLD and its early progression affects the distribution of lipids in the liver, producing lipotoxicity and inflammation. In this study, we used dietary and genetic mouse models of NAFL and NASH and translated the results to humans by correlating the spatial distribution of lipids in liver tissue with disease progression using advanced mass spectrometry imaging technology. We identified several lipids with distinct zonal distributions in control and NAFL samples and observed partial to complete loss of lipid zonation in NASH. In addition, we found increased hepatic expression of genes associated with remodeling the phospholipid membrane, release of arachidonic acid (AA) from the membrane, and production of eicosanoid species that promote inflammation and cell injury. The results of our immunohistochemistry analyses suggest that the zonal location of remodeling enzyme LPCAT2 plays a role in the change in spatial distribution for AA‐containing lipids. This results in a cycle of AA‐enrichment in pericentral hepatocytes, membrane release of AA, and generation of proinflammatory eicosanoids and may account for increased oxidative damage in pericentral regions in NASH. Conclusion: NAFLD is associated not only with lipid enrichment, but also with zonal changes of specific lipids and their associated metabolic pathways. This may play a role in the heterogeneous development of NAFLD. (Hepatology 2017;65:1165‐1180)

Background and Aims: Hepatocytes undergo profound metabolic rewiring when primed to proliferate during compensatory regeneration and in hepatocellular carcinoma (HCC). However, the metabolic control of these processes is not fully understood. In order to capture the metabolic signature of proliferating hepatocytes, we applied state‐of‐the‐art systems biology approaches to models of liver regeneration, pharmacologically and genetically activated cell proliferation, and HCC. Approach and Results: Integrating metabolomics, lipidomics, and transcriptomics, we link changes in the lipidome of proliferating hepatocytes to altered metabolic pathways including lipogenesis, fatty acid desaturation, and generation of phosphatidylcholine (PC). We confirm this altered lipid signature in human HCC and show a positive correlation of monounsaturated PC with hallmarks of cell proliferation and hepatic carcinogenesis. Conclusions: Overall, we demonstrate that specific lipid metabolic pathways are coherently altered when hepatocytes switch to proliferation. These represent a source of targets for the development of therapeutic strategies and prognostic biomarkers of HCC.

To date most global approaches to functional genomics have centred on genomics, transcriptomics and proteomics. However, since a number of high-profile publications, interest in metabolomics, the global profiling of metabolites in a cell, tissue or organism, has been rapidly increasing. A range of analytical techniques, including ¹ H NMR spectroscopy, gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS), Fourier Transform mass spectrometry (FT–MS), high performance liquid chromatography (HPLC) and electrochemical array (EC-array), are required in order to maximize the number of metabolites that can be identified in a matrix. Applications have included phenotyping of yeast, mice and plants, understanding drug toxicity in pharmaceutical drug safety assessment, monitoring tumour treatment regimes and disease diagnosis in human populations. These successes are likely to be built on as other analytical and bioinformatic approaches are developed to fully exploit the information obtained in metabolic profiles. To assist in this process, databases of metabolomic data will be necessary to allow the passage of information between laboratories. In this prospective review, the capabilities of metabolomics in the field of medicine will be assessed in an attempt to predict the impact this ‘Cinderella approach’ will have at the ‘functional genomic ball’.

The functional genomic techniques of transcriptomics and proteomics promise unparalleled global information during the drug development process. However, if these technologies are used in isolation the large multivariate data sets produced are often difficult to interpret, and have the potential of missing key metabolic events (e.g. as a result of experimental noise in the system). To better understand the significance of these megavariate data the temporal changes in phenotype must be described. High resolution 1H NMR spectroscopy used in conjunction with pattern recognition provides one such tool for defining the dynamic phenotype of a cell, organ or organism in terms of a metabolic phenotype. In this review the benefits of this metabonomics / nmetabolomics approach to problems in toxicology will be discussed. One of the major benefits of this approach is its high throughput nature and cost effectiveness on a per sample basis. Using such a method the consortium for metabonomic toxicology (COMET) are currently investigating ∼150 model liver and kidney toxins. This investigation will allow the generation of expert systems where liver and kidney toxicity can be predicted for model drug compounds, providing a new research tool in the field of drug metabolism. The review will also include how metabonomics may be used to investigate co-responses with transcripts and proteins involved in metabolism and stress responses, such as during drug induced fatty liver disease. By using data integration to combine metabolite analysis and gene expression profiling key perturbed metabolic pathways can be identified and used as a tool to investigate drug function.