Hepatic steatosis is often associated with insulin resistance and obesity and can lead to steatohepatitis and cirrhosis. In this study, we have demonstrated that hormone-sensitive lipase (HSL) and adipose triglyceride lipase (ATGL), two enzymes critical for lipolysis in adipose tissues, also contribute to lipolysis in the liver and can mobilize hepatic triglycerides in vivo and in vitro. Adenoviral overexpression of HSL and/or ATGL reduced liver triglycerides by 40 -60% in both ob/ob mice and mice with high fat diet-induced obesity. However, these enzymes did not affect fasting plasma triglyceride and free fatty acid levels or triglyceride and apolipoprotein B secretion rates. Plasma 3-␤-hydroxybutyrate levels were increased 3-5 days after infection in both HSL-and ATGL-overexpressing male mice, suggesting an increase in ␤-oxidation. Expression of genes involved in fatty acid transport and synthesis, lipid storage, and mitochondrial bioenergetics was unchanged. Mechanistic studies in oleate-supplemented McA-RH7777 cells with adenoviral overexpression of HSL or ATGL showed that reduced cellular triglycerides could be attributed to increases in ␤-oxidation as well as direct release of free fatty acids into the medium. In summary, hepatic overexpression of HSL or ATGL can promote fatty acid oxidation, stimulate direct release of free fatty acid, and ameliorate hepatic steatosis. This study suggests a direct functional role for both HSL and ATGL in hepatic lipid homeostasis and identifies these enzymes as potential therapeutic targets for ameliorating hepatic steatosis associated with insulin resistance and obesity. Nonalcoholic fatty liver disease (NAFLD)4 is often associated with obesity, insulin resistance, and metabolic syndrome (1, 2). Nonalcoholic steatohepatitis (NASH), the more virulent form of NAFLD, can lead to cirrhosis. Current treatments for subjects with NAFLD are usually directed at alleviating the associated metabolic symptoms of the patients (3). Insulin sensitizers such as thiazolidinediones or metformin improve insulin sensitivity with concomitant reduction of liver fat contents in human and mouse models (3-5). The amelioration of hepatic steatosis by these agents is likely secondary to improved insulin sensitivity. Imbalances between the input, oxidation, synthesis, and output of fatty acids (FA) all could contribute to hepatic steatosis, and dysregulation of each pathway has been documented in animal models (6). For example, leptin-deficient ob/ob mice are insulin-resistant, dyslipidemic, and have fatty livers despite the up-regulation of FA oxidation genes (7, 8) and increases in mitochondrial and peroxisomal ␤-oxidation (9). Hepatic steatosis in these animals is attributed to the up-regulation of sterol-responsive element-binding protein (SREBP) 1c, a master regulator of lipogenesis (10), and the consequent increase in de novo lipogenesis (11,12). FA uptake in the liver is also likely increased as genes involved in FA uptake and transport (e.g. CD36) are up-regulated in these animals (13). NAF...

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Although studies in vitro and in hypothyroid animals show that thyroid hormone can, under some circumstances, modulate the actions of low-density lipoprotein (LDL) receptors, the mechanisms responsible for thyroid hormone's lipid-lowering effects are not completely understood. We tested whether LDL receptor (LDLR) expression was required for cholesterol reduction by treating control and LDLR-knockout mice with two forms of thyroid hormone T(3) and 3,5-diiodo-l-thyronine. High doses of both 3,5-diiodo-l-thyronine and T(3) dramatically reduced circulating total and very low-density lipoprotein/LDL cholesterol (∼70%) and were associated with reduced plasma T(4) level. The cholesterol reduction was especially evident in the LDLR-knockout mice. Circulating levels of both apolipoprotein B (apo)B48 and apoB100 were decreased. Surprisingly, this reduction was not associated with increased protein or mRNA expression of the hepatic lipoprotein receptors LDLR-related protein 1 or scavenger receptor-B1. Liver production of apoB was markedly reduced, whereas triglyceride production was increased. Thus, thyroid hormones reduce apoB lipoproteins via a non-LDLR pathway that leads to decreased liver apoB production. This suggests that drugs that operate in a similar manner could be a new therapy for patients with genetic defects in the LDLR.

In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.

Timely identification of emerging antigenic variants is critical to influenza vaccine design. The accuracy of a sequence-based antigenic prediction method relies on the choice of amino acids substitution matrices. In this study, we first compared a comprehensive 95 substitution matrices reflecting various amino acids properties in predicting the antigenicity of influenza viruses by a random forest model. We then proposed a novel algorithm called joint random forest regression (JRFR) to jointly consider top substitution matrices. We applied JRFR to human H3N2 seasonal influenza data from 1968 to 2003. A 10-fold cross-validation shows that JRFR outperforms other popular methods in predicting antigenic variants. In addition, our results suggest that structure features are most relevant to influenza antigenicity. By restricting the analysis to data involving two adjacent antigenic clusters, we inferred a few key amino acids mutation driving the 11 historical antigenic drift events, pointing to experimentally validated mutations. Finally, we constructed an antigenic cartography of all H3N2 viruses with hemagglutinin (the glycoprotein on the surface of the influenza virus responsible for its binding to host cells) sequence available from NCBI flu database, and showed an overall correspondence and local inconsistency between genetic and antigenic evolution of H3N2 influenza viruses.