Yuping Zhou1, Xueyou Lv1, Hui Qu1, Kekai Zhao1, Liyun Fu2, Linwen Zhu3,4, Guoliang Ye1, Junming Guo3,4. 1. Department of Gastroenterology, The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China. 2. Department of Hepatology, Ningbo No. 2 Hospital, Medical School of Ningbo University, Ningbo, China. 3. Institute of Biochemistry and Molecular Biology, Medical School of Ningbo University, Ningbo, China. 4. Zhejiang Province Key Laboratory of Pathophysiology Technology Research, Ningbo, China.
Abstract
AIM: To measure the expression profile of circular RNA (circRNA) in hepatic tissues in a liver fibrosis model and to explore their function using molecular biology and bioinformatic techniques. METHODS: The classic CCl4 mouse liver fibrosis model was established alongside a normal control group. The circRNA expression profile of hepatic tissue from the two groups was compared using a high-throughput circRNA microarray. The differentially expressed circRNAs were identified, and real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify a subset of the differentially expressed circRNAs (target genes). At the same time, the mouse oxidative stress injury, macrophage inflammation, and hepatic stellate cell activation models were established, and the expression of target circRNA in the above cells was measured by RT-qPCR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to predict the biological functions of target genes. Finally, one of the circRNAs was selected and its cellular function was verified using siRNA. RESULTS: A total of 10 389 circRNAs were analyzed by microarray. Compared with the normal group, there were 69 circRNAs that were differentially expressed in the liver fibrosis model group (>2-fold differential expression, P < 0.05), of which 14 were upregulated and 55 were downregulated. Five circRNAs and their differential expression were verified by RT-qPCR, and the findings were consistent with the microarray results. Of these, three circRNAs were differentially expressed (P < 0.05) in the JS1 model, one circRNA was differentially expressed (P < 0.05) in the AML12 model, and four circRNAs were differentially expressed (P < 0.05) in the RAW264.7 model. The GO analysis showed that the differentially expressed circRNAs might be involved in cell autophagy, composition of extracellular matrix components, synthesis and metabolism of retinoic acid, retinol dehydrogenase activity, ubiquitin-like protein ligase activity, histone methylation, and other biological functions. The KEGG analysis showed that the target genes of the differentially expressed circRNAs might be involved in transforming growth factor-β1/smads, Hippo, Rap1, vascular endothelial growth factor, and other signaling pathways. Lipofection experiments showed that the expression of α-smooth muscle actin (α-SMA) in JS1 cells increased significantly after the expression of mmu_circ_34116 was decreased. CONCLUSION: The circRNA expression profile in liver fibrosis tissue shows significant changes. Partially differentially expressed circRNA could be involved in hepatic fibrosis related to hepatic oxidative stress injury, macrophage inflammation, and stellate cell activation. For instance, mmu_circ_34116 can significantly inhibit the activation of hepatic stellate cells.
AIM: To measure the expression profile of circular RNA (circRNA) in hepatic tissues in a liver fibrosis model and to explore their function using molecular biology and bioinformatic techniques. METHODS: The classic CCl4mouseliver fibrosis model was established alongside a normal control group. The circRNA expression profile of hepatic tissue from the two groups was compared using a high-throughput circRNA microarray. The differentially expressed circRNAs were identified, and real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify a subset of the differentially expressed circRNAs (target genes). At the same time, the mouse oxidative stress injury, macrophage inflammation, and hepatic stellate cell activation models were established, and the expression of target circRNA in the above cells was measured by RT-qPCR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to predict the biological functions of target genes. Finally, one of the circRNAs was selected and its cellular function was verified using siRNA. RESULTS: A total of 10 389 circRNAs were analyzed by microarray. Compared with the normal group, there were 69 circRNAs that were differentially expressed in the liver fibrosis model group (>2-fold differential expression, P < 0.05), of which 14 were upregulated and 55 were downregulated. Five circRNAs and their differential expression were verified by RT-qPCR, and the findings were consistent with the microarray results. Of these, three circRNAs were differentially expressed (P < 0.05) in the JS1 model, one circRNA was differentially expressed (P < 0.05) in the AML12 model, and four circRNAs were differentially expressed (P < 0.05) in the RAW264.7 model. The GO analysis showed that the differentially expressed circRNAs might be involved in cell autophagy, composition of extracellular matrix components, synthesis and metabolism of retinoic acid, retinol dehydrogenase activity, ubiquitin-like protein ligase activity, histone methylation, and other biological functions. The KEGG analysis showed that the target genes of the differentially expressed circRNAs might be involved in transforming growth factor-β1/smads, Hippo, Rap1, vascular endothelial growth factor, and other signaling pathways. Lipofection experiments showed that the expression of α-smooth muscle actin (α-SMA) in JS1 cells increased significantly after the expression of mmu_circ_34116 was decreased. CONCLUSION: The circRNA expression profile in liver fibrosis tissue shows significant changes. Partially differentially expressed circRNA could be involved in hepatic fibrosis related to hepatic oxidative stress injury, macrophage inflammation, and stellate cell activation. For instance, mmu_circ_34116 can significantly inhibit the activation of hepatic stellate cells.
Authors: Ming Qiao; Jianhua Yang; Yao Zhao; Yi Zhu; Xiaomei Wang; Xinling Wang; Junping Hu Journal: Evid Based Complement Alternat Med Date: 2020-02-18 Impact factor: 2.629