| Literature DB >> 35890194 |
Yahong Shi1,2, Tun Yan1,2,3, Xi Lu1,2, Kai Li1,2, Yifeng Nie2, Chuqiao Jiao4, Huizhen Sun1,2, Tingting Li1,2, Xiang Li2, Dong Han2.
Abstract
Liver fibrosis is an urgent public health problem which is difficult to resolve. However, various drugs for the treatment of liver fibrosis in clinical practice have their own problems during use. In this study, we used phloridzin to treat hepatic fibrosis in the CCl4-induced C57/BL6N mouse model, which was extracted from lychee core, a traditional Chinese medicine. The therapeutic effect was evaluated by biochemical index detections and ultrasound detection. Furthermore, in order to determine the mechanism of phloridzin in the treatment of liver fibrosis, we performed high-throughput sequencing of mRNA and lncRNA in different groups of liver tissues. The results showed that compared with the model group, the phloridzin-treated groups revealed a significant decrease in collagen deposition and decreased levels of serum alanine aminotransferase, aspartate aminotransferase, laminin, and hyaluronic acid. GO and KEGG pathway enrichment analysis of the differential mRNAs was performed and revealed that phloridzin mainly affects cell ferroptosis. Gene co-expression analysis showed that the target genes of lncRNA were obvious in cell components such as focal adhesions, intercellular adhesion, and cell-substrate junctions and in metabolic pathways such as carbon metabolism. These results showed that phloridizin can effectively treat liver fibrosis, and the mechanism may involve ferroptosis, carbon metabolism, and related changes in biomechanics.Entities:
Keywords: biomechanics; energy metabolism; ferroptosis; liver fibrosis; lncRNA; mRNA; phloridzin
Year: 2022 PMID: 35890194 PMCID: PMC9321461 DOI: 10.3390/ph15070896
Source DB: PubMed Journal: Pharmaceuticals (Basel) ISSN: 1424-8247
Figure 1The compound phloridzin derived from lychee seed in the treatment of liver fibrosis.
Figure 2Pharmacodynamic results of phloridzin in the treatment of liver fibrosis. (A) Representative photos of liver appearance in each group. (B) Detailed view of liver appearance in each group. (C) Histological images of liver in each group after H&E stain. (D) Histological images of liver in each group after Sirius red stain. (E) Representative in vivo ultrasound imaging of livers from different groups. Fibrosis improvement can be seen by a decreased intensity in hepatic echogenicity. The 3D surface plots of the ultrasound images within the orange-lined squares correspond to the echogenic uniformity in the liver. (F) Statistics of the liver brightness of each group shows that compared with the model group, each treatment group has different degrees of reduction. Among them, the results of the silibinin treatment group and the high-dose phloridzin group are significantly different. (G) Percentage of area occupied by fibers in Sirius red-stained sections of liver in each group. (H,I) AST and ALT levels of each group were detected. (J,K) LN and HA levels of each group were detected by ELISA.
Figure 3The statistics of DE mRNAs between control group vs model group, silibinin group vs. model group, and phloridzin vs. model group. DE mRNAs in the samples are shown using a heat map (A), bar chart (B), Venn upset diagram (C), and Venn diagram (D).
Figure 4GO and KEGG pathway analysis explain the different mechanism between silibinin and phloridzin. (A–C) Top 10 terms of GO enrichment analysis of DEG mRNAs between silibinin group and model group. (D–F) Top 10 terms of GO enrichment analysis of DEG mRNAs between phloridzin group and model group. (G,H) Top 20 terms of KEGG pathway enrichment analysis of DEG mRNAs between silibinin group and model group. (I,J) Top 20 terms of KEGG pathway enrichment analysis of DEG mRNAs between phloridzin group and model group.
Figure 5lncRNA-mRNA interaction and lncRNA target gene prediction. A visual regulatory nework for the lncRNA-mRNA relationship was drawn from target gene prediction results by Ctoscape 3.7.2, shown in (A). Then, we preformed the GO and KEGG pathway analysis based on the predicted mRNAs. (B) shows the top 30 terms of GO analysis and (C) shows the top 30 terms of KEGG pathway analysis.