ApoE-/-高脂模型小鼠肝脏转录组铁死亡潜在分子网络互作的初步探讨

Preliminary study on the potential molecular net work interaction of ferroptosis in liver transcriptome of ApoE-/-hyperlipidemic mice

摘要:

目的： 对ApoE^-/-高脂模型小鼠肝脏中铁死亡相关差异基因的生物过程和信号通路进行分析，构建肝脏内铁死亡的分子调控网络，从转录水平揭示高脂血症的发病机制。方法： C57BL/6J小鼠7只作为空白对照组，ApoE^-/-小鼠7只作为模型组，对照组和模型组分别给予普通饲料和高脂饲料，16周后检测小鼠的甘油三酯（triglyceride，TG）、胆固醇（cholesterol，TC）、低密度脂蛋白（low-density lipoprotein，LDL-C）、高密度脂蛋白（high-density lipoprotein，HDL-C）血清水平，并对肝脏取材进行HE染色及转录组分析。应用Graph-Pad Prism 8.0.2软件分析铁死亡相关差异基因分布情况，通过STRING11.0平台构建蛋白-蛋白相互作用网络模型，DAVID数据库进行GO生物功能富集和KEGG通路富集，Cytoscape的ClueGO插件对铁死亡相关差异基因的功能及代谢通路进行可视化分析。结果： 与对照组TG（1.19±0.09）、TC（2.31±0.15）、LDL-C（0.29±0.05）、HDL-C（1.68±0.06）相比，模型组TG（1.75±0.08）、TC（38.80±4.03）、LDL-C（36.27±3.80）水平明显升高（P=0.000、0.000、0.000），HDL-C（1.26±0.05）水平明显降低（P=0.000）。HE染色结果表明，模型组与对照组相比肝细胞肿胀变性明显且伴有大量脂肪空泡。肝脏转录组分析筛选出3个铁死亡差异基因铁反应元件结合蛋白2（iron-responsive element-binding protein 2，IREB2）、铁蛋白轻链（ferritin light chain，FTL）、铁蛋白重链（ferritin heavy chain，FTH1），PPI网络分析并在肝脏差异基因中校对，共筛选出铁死亡相关差异基因23个，差异基因中上调基因6个，下调基因20个。网络关系分析表明，铁死亡相关差异基因蛋白相互关联并彼此调节。利用DAVID数据库共筛选出36个生物过程和5条信号通路参与铁死亡。结论： 揭示肝脏组织中铁死亡可以通过多进程、多通路对高脂血症进行调控，同时高脂可促进铁死亡的发生，为从转录水平揭示高脂血症的发病机制提供了参考依据。

Abstract:

Objective: To analyze the biological processes and signal pathways of differential genes related to ferroptosis in the liver of ApoE^-/-hyperlipidemic mice, to construct a molecular regulatory network of liver ferroptosis, and to reveal the pathogenesis of hyperlipidemia at the transcriptional level. Methods: This study selected 7 C57BL/6J mice as control group and 7 ApoE^-/-mice as model group. The control group and model group were fed with normal diet and high-fat diet respectively. After 16 weeks, the serum levels of triglyceride (TG), cholesterol (TC), low-density lipoprotein (LDL-C) and high-density lipoprotein (HDL-C) were detected, and the liver samples were analyzed by HE staining and transcriptome analysis. The distribution of ferroptosis-related differential genes was analyzed by Graph-Pad Prism 8.0.2 software. The protein-protein interaction network model was constructed by STRING11.0 platform. GO biological function enrichment and KEGG pathway enrichment were carried out in DAVID database. The ClueGO plug-in of Cytoscape was used to visually analyze the function and metabolic pathway of ferroptosis-related differential genes. Results: Compared with the control group, TG (1.19±0.09), TC (2.31±0.15), LDL-C (0.29±0.05), HDL-C (1.68±0.06), the levels of TG (1.75±0.08), TC (38.80±4.03), LDL-C (36.27±3.80) in the model group significantly increased (P=0.000, 0.000, 0.000), and the level of HDL-C (1.26±0.05) significantly decreased (P=0.000) . The results of HE staining showed that compared with the control group, the swelling and degeneration of hepatocytes in the model group was obvious and accompanied by a large number of fat vacuoles. Three differential genes of ferroptosis, IREB2 (iron-responsive element-binding protein 2), FTL (ferritin light chain), and FTH1 (ferritin heavy chain 1), were screened by liver transcriptome analysis and calibrated in liver differential genes by PPI network analysis. A total of 23 ferroptosis related genes were screened, including6up-regulatedgenesand20down-regulated genes. Network relationship analysis showed that ferroptosis-related differential gene proteins were interrelated and regulated with each other. Through DAVID database, 36 biological processes and 5 signaling pathways participating in ferroptosis were screened out. Conclusion: It is revealed that ferroptosis in liver tissue can regulate hyperlipidemia through multi-processes and multi-pathways, and high fat can promote the occurrence of ferroptosis, which provides a reference basis for revealing the pathogenesis of hyperlipidemia at the transcriptional level.