Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
Cell Syst. 2018 Jan 24;6(1):7-9. doi: 10.1016/j.cels.2018.01.004.
Multi-omics multi-tissue data are used to interpret genome-wide association study results from mice to identify key driver genes of non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease (NAFLD) is the accumulation of fat (steatosis) in the liver due to causes other than excessive alcohol consumption. The disease may progress to more severe forms of liver diseases, including non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. In this issue of Cell Systems, Krishnan et al. (2018) reveal mechanisms underlying NAFLD by generating multi-omics data using liver and adipose tissues obtained from the Hybrid Mouse Diversity Panel, consisting of 113 mouse strains with various degrees of NAFLD. The study identified key driver genes of NAFLD that can be used in the development of efficient treatment strategies and illustrates the potential utility of systematic analysis of multi-layer biological networks.
多组学多组织数据被用于解释来自小鼠的全基因组关联研究结果，以鉴定非酒精性脂肪性肝病的关键驱动基因。非酒精性脂肪性肝病（NAFLD）是由于除过量饮酒以外的原因导致肝脏脂肪（脂肪变性）堆积。该疾病可能进展为更严重的肝脏疾病，包括非酒精性脂肪性肝炎、肝硬化和肝细胞癌。在本期《Cell Systems》中，Krishnan 等人（2018 年）通过利用源自杂交小鼠多样性面板的肝脏和脂肪组织生成多组学数据，揭示了 NAFLD 的发生机制。该面板由 113 个具有不同程度 NAFLD 的小鼠品系组成。该研究鉴定了 NAFLD 的关键驱动基因，可用于开发有效的治疗策略，并说明了系统分析多层次生物网络的潜在效用。
