Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia.
Computational Biology Group, Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia.
Int J Mol Sci. 2021 Jan 15;22(2):832. doi: 10.3390/ijms22020832.
Multifactorial metabolic diseases, such as non-alcoholic fatty liver disease, are a major burden to modern societies, and frequently present with no clearly defined molecular biomarkers. Herein we used system medicine approaches to decipher signatures of liver fibrosis in mouse models with malfunction in genes from unrelated biological pathways: cholesterol synthesis-, notch signaling-, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling-, and unknown lysosomal pathway-. Enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome and TRANScription FACtor (TRANSFAC) databases complemented with genome-scale metabolic modeling revealed fibrotic signatures highly similar to liver pathologies in humans. The diverse genetic models of liver fibrosis exposed a common transcriptional program with activated estrogen receptor alpha (ERα) signaling, and a network of interactions between regulators of lipid metabolism and transcription factors from cancer pathways and the immune system. The novel hallmarks of fibrosis are downregulated lipid pathways, including fatty acid, bile acid, and steroid hormone metabolism. Moreover, distinct metabolic subtypes of liver fibrosis were proposed, supported by unique enrichment of transcription factors based on the type of insult, disease stage, or potentially, also sex. The discovered novel features of multifactorial liver fibrotic pathologies could aid also in improved stratification of other fibrosis related pathologies.
多因素代谢性疾病,如非酒精性脂肪性肝病,是现代社会的主要负担,并且通常没有明确定义的分子生物标志物。在此,我们使用系统医学方法来破译来自不同生物学途径的基因功能障碍的小鼠模型中的肝纤维化特征:胆固醇合成、Notch 信号、核因子 kappa 轻链增强子的 B 细胞(NF-κB)信号和未知溶酶体途径。京都基因与基因组百科全书(KEGG)、反应组和转录因子(TRANSFAC)数据库的富集分析,以及基因组规模的代谢建模,补充了与人类肝脏病理学高度相似的纤维化特征。不同的肝纤维化遗传模型揭示了一个共同的转录程序,即激活的雌激素受体α(ERα)信号,以及脂质代谢调节剂和癌症途径以及免疫系统转录因子之间的相互作用网络。纤维化的新特征是下调的脂质途径,包括脂肪酸、胆汁酸和类固醇激素代谢。此外,提出了不同的肝纤维化代谢亚型,这得益于基于损伤类型、疾病阶段或潜在的性别而独特富集的转录因子。多因素肝纤维化病理的这些新发现的特征也有助于改善其他纤维化相关病理的分层。