Liver Perilipin 5 Expression Worsens Hepatosteatosis But Not Insulin Resistance in High Fat-Fed Mice

Perilipin 5 (PLIN5) is a lipid droplet (LD) protein highly expressed in oxidative tissues, including the fasted liver. However, its expression also increases in nonalcoholic fatty liver. To determine whether PLIN5 regulates metabolic phenotypes of hepatosteatosis under nutritional excess, liver targeted overexpression of PLIN5 was achieved using adenoviral vector (Ad-PLIN5) in male C57BL/6J mice fed high-fat diet. Mice treated with adenovirus expressing green fluorescent protein (GFP) (Ad-GFP) served as control. Ad-PLIN5 livers increased LD in the liver section, and liquid chromatography with tandem mass spectrometry revealed increases in lipid classes associated with LD, including triacylglycerol, cholesterol ester, and phospholipid classes, compared with Ad-GFP liver. Lipids commonly associated with hepatic lipotoxicity, diacylglycerol, and ceramides, were also increased in Ad-PLIN5 liver. The expression of genes in lipid metabolism regulated by peroxisome proliferator-activated receptor-alpha was reduced suggestive of slower mobilization of stored lipids in Ad-PLIN5 mice. However, the increase of hepatosteatosis by PLIN5 overexpression did not worsen glucose homeostasis. Rather, serum insulin levels were decreased, indicating better insulin sensitivity in Ad-PLIN5 mice. Moreover, genes associated with liver injury were unaltered in Ad-PLIN5 steatotic liver compared with Ad-GFP control. Phosphorylation of protein kinase B was increased in Ad-PLIN5-transduced AML12 hepatocyte despite of the promotion of fatty acid incorporation to triacylglycerol as well. Collectively, our data indicates that the increase in liver PLIN5 during hepatosteatosis drives further lipid accumulation but does not adversely affect hepatic health or insulin sensitivity

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Abstract 970A: Using a radiogenomic approach to classify pancreatic cancer precursors

Abstract Background: Intraductal papillary mucinous neoplasms (IPMNs) are cystic pancreatic cancer precursors incidentally detected by imaging in more than 75,000 Americans each year. There is an unmet need to discover noninvasive approaches to differentiate ‘benign’ IPMNs that can be monitored from ‘malignant’ IPMNs that warrant surgery. We previously developed a blood-based ‘miRNA genomic classifier (MGC)’ that helps predict malignant IPMN pathology. The goal of this study was to evaluate whether novel radiomic features from preoperative computed tomography (CT) scans may improve prediction of IPMN pathology beyond that provided by standard radiologic features, either alone or in combination with the MGC. Methods: Preoperative CT images were obtained for 37 surgically-resected, pathologically-confirmed IPMN cases with matched preoperative miRNA expression data. Images were reviewed for standard radiologic features characterized to be ‘high-risk’ or ‘worrisome’ for malignancy according to consensus guidelines. The region of interest within the pancreas was identified and segmented using a semi-automated algorithm. A total of 112 two-dimensional (2D) quantitative texture features (which measure tumor size, shape, and location) and non-texture features (which measure smoothness, coarseness, and regularity) were extracted. Logistic regression models were used to explore associations between non-redundant radiomic features and IPMN pathology. Principal component analysis was also performed to generate an index score (defined by the first principal component of the most promising radiomic features) that was evaluated for its association with malignant pathology. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the new radiomic features individually and in combination with the MGC were estimated and compared to values obtained for standard radiologic features. Results: The MGC and standard ‘high-risk’ and ‘worrisome’ radiologic features had area under the receiver operating characteristic curve (AUC) values of 0.83, 0.84, and 0.54, respectively. Analysis of 112 extracted preoperative radiomic CT features revealed 14 textural and non-textural features that differentiated malignant from benign IPMNs (P&amp;lt;0.05). Collectively, the 14 radiomic features had an AUC = 0.77. A model that combined radiomic features and the MGC had an AUC = 0.92 and estimates of sensitivity (83%), specificity (89%), PPV (88%), and NPV (85%) that were superior to models not based on these novel data types. Conclusions: Our preliminary findings suggest that clinical decision-making models that integrate novel quantitative radiomic features with a blood-based miRNA classifier may more accurately predict IPMN pathology than models that rely on standard clinical data or worrisome radiologic features alone. Larger, prospective, multi-center studies are planned to explore this topic further. Citation Format: Jennifer B. Permuth, Jung Choi, Yoganand Balarunathan, Jongphil Kim, Dung-Tsa Chen, Kun Jiang, Sonia Orcutt, Lu Chen, Kimberly Quinn, Rodrigo Carvajal, Guillermo Gonzalez-Calderon, Michelle Fournier, Mahmoud Abdalla, Alberto Garcia, Amber Bouton, Danny Yakoub, Suzanne Lechner, Jose Trevino, Nipun Merchant, Robert Gillies, Mokenge Malafa. Using a radiogenomic approach to classify pancreatic cancer precursors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 970A.</jats:p