Analysis of Gene Expression Profiles in the Liver of Rats With Intrauterine Growth Retardation - Shen, Zheng; Zhu, Weifen; Du, Lizhong (Frontiers in Pediatrics, 2022)
Background: Intrauterine growth restriction (IUGR) is not only an important cause of fetal and neonatal morbidity and mortality but also is associated with increased risk of developing metabolic diseases later in life. The inner mechanism underlying this increased risk remains unclear. We compared differentially expressed genes between the liver of appropriate for gestational age (AGA) and IUGR rat model and identified their impact on molecular pathways associated with metabolic syndrome. Methods: We extracted RNA from liver of IUGR and AGA rats and profiled gene expression by microarray analysis. Enrichment analyses were performed on the GO and KEGG pathway using the Search Tool for the Retrieval of Interacting Genes database. Subsequently, the regulatory interaction network of IUGR-associated genes was visualized using Cytoscape software. Further, quantitative reverse transcriptase PCR was used to verify the results. Results: A total of 815 genes were identified to be significantly differentially expressed (fold-change>1.5, P value<0.05) between IUGR and AGA, with 347 genes upregulated and 468 downregulated in IUGR compared with AGA. GO, KEGG enrichment analyses and protein-protein interaction network analysis of target genes were performed. The results discovered that core genes including Ppargc1a, Prkaa2, Slc2a1, Rxrg and Gcgr, and pathways, including PPAR signaling pathway, FoxO signaling pathway, had a potential relationship with the development of metabolic syndrome in IUGR. We also confirmed that five genes involving in glycometabolism were differentially expressed at the mRNA level between IUGR and AGA. Conclusion: The results of the present study provide a comprehensive understanding of differential gene expression patterns in IUGR and AGA. Our pilot study may gain a deeper understanding of the pathogenesis of metabolic syndrome caused by IUGR. The suggestive candidates may serve as potential biomarkers and eventually to treat them appropriately.