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一种基于网络的方法揭示了非酒精性脂肪性肝病中失调的转录调控。

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease.

作者信息

Yang Hong, Arif Muhammad, Yuan Meng, Li Xiangyu, Shong Koeun, Türkez Hasan, Nielsen Jens, Uhlén Mathias, Borén Jan, Zhang Cheng, Mardinoglu Adil

机构信息

Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.

Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey.

出版信息

iScience. 2021 Oct 5;24(11):103222. doi: 10.1016/j.isci.2021.103222. eCollection 2021 Nov 19.

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. We performed network analysis to investigate the dysregulated biological processes in the disease progression and revealed the molecular mechanism underlying NAFLD. Based on network analysis, we identified a highly conserved disease-associated gene module across three different NAFLD cohorts and highlighted the predominant role of key transcriptional regulators associated with lipid and cholesterol metabolism. In addition, we revealed the detailed metabolic differences between heterogeneous NAFLD patients through integrative systems analysis of transcriptomic data and liver-specific genome-scale metabolic model. Furthermore, we identified transcription factors (TFs), including SREBF2, HNF4A, SREBF1, YY1, and KLF13, showing regulation of hepatic expression of genes in the NAFLD-associated modules and validated the TFs using data generated from a mouse NAFLD model. In conclusion, our integrative analysis facilitates the understanding of the regulatory mechanism of these perturbed TFs and their associated biological processes.

摘要

非酒精性脂肪性肝病(NAFLD)是全球慢性肝病的主要病因。我们进行了网络分析,以研究疾病进展中失调的生物学过程,并揭示了NAFLD的分子机制。基于网络分析,我们在三个不同的NAFLD队列中鉴定出一个高度保守的疾病相关基因模块,并强调了与脂质和胆固醇代谢相关的关键转录调节因子的主要作用。此外,我们通过对转录组数据和肝脏特异性基因组规模代谢模型的综合系统分析,揭示了异质性NAFLD患者之间详细的代谢差异。此外,我们鉴定出转录因子(TFs),包括SREBF2、HNF4A、SREBF1、YY1和KLF13,它们显示出对NAFLD相关模块中肝脏基因表达的调节作用,并使用从小鼠NAFLD模型生成的数据验证了这些TFs。总之,我们的综合分析有助于理解这些受干扰的TFs及其相关生物学过程的调节机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1837/8529555/9e99b3586c6a/fx1.jpg

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