Analysis of Gene Expression Profiles in the Liver of Rats With Intrauterine Growth Retardation

Abstract

Background: Intrauterine growth restriction (IUGR) is highly associated with fetal as well as neonatal morbidity, mortality, and an increased risk metabolic disease development later in life. The mechanism involved in the increased risk has not been established. We compared differentially expressed genes between the liver of appropriate for gestational age (AGA) and IUGR rat models and identified their effects on molecular pathways involved in the metabolic syndrome.

Methods: We extracted RNA from the liver of IUGR and AGA rats and profiled gene expression by microarray analysis. GO function and KEGG pathway enrichment analyses were conducted using the Search Tool for the Retrieval of Interacting Genes database. Then, the Cytoscape software was used to visualize regulatory interaction networks of IUGR-related genes. The results were further verified via quantitative reverse transcriptase PCR analysis.

Results: In this study, 815 genes were found to be markedly differentially expressed (fold-change >1.5, p < 0.05) between IUGR and AGA, with 347 genes elevated and 468 suppressed in IUGR, relative to AGA. Enrichment and protein-protein interaction network analyses of target genes revealed that core genes including Ppargc1a, Prkaa2, Slc2a1, Rxrg, and Gcgr, and pathways, including the PPAR signaling pathway and FoxO signaling pathway, had a potential association with metabolic syndrome development in IUGR. We also confirmed that at the mRNA level, five genes involved in glycometabolism were differentially expressed between IUGR and AGA.

Conclusion: Our findings elucidate on differential gene expression profiles in IUGR and AGA. Moreover, they elucidate on the pathogenesis of IUGR-associated metabolic syndromes. The suggested candidates are potential biomarkers and eventually intended to treat them appropriately.

Keywords: IUGR; differentially expressed genes; liver; metabolic syndrome; microarray analysis.

Figure 1

The body weight of 85 AGA (6.49 ± 0.35) and 80 IUGR (4.55 ± 0.42) rats at 1 day (**P < 0.01).

Figure 2

Microarray gene analysis. (A) Hierarchical cluster analyses of DEGs. Each probe set is denoted by a single row of colored bars. Red: upregulated; green: downregulated; black: no change. Every line denotes a liver sample from control (n = 5) and IUGR (n = 5) newborn rats. (B) The volcano plot analysis of DEGs. Green color denotes upregulation; Red color represents downregulation. Genes with a significant change of more/ <1.5-fold were selected.

Figure 3

Results of GO analysis. (A) Top 10 GO biological processes, (B) Top 10 GO MF, and (C) Top 4 GO CC in IUGR compared with AGA.

Figure 4

Results of KEGG analysis: Top 10 KEGG pathways in IUGR compared with AGA.

Figure 5

Downregulation of the PPAR signaling pathway in IUGR. We obtained the original Figure 5 from the KEGG website (https://www.kegg.jp/kegg-bin/show_pathway?map03320), and we made some modifications.

Figure 6

PPI network analysis of DEGs in IUGR. The dots indicate individual differentially expressed genes, and the lines between any nodes represent the interrelations of those proteins. The PPI network was established using cut-off values of confidence score >0.7 using default online parameter settings. We visualized the PPI network analysis of DEGs from the STRING database (version 10.5; http://string-db.org/).

Figure 7

Validation of DEGs by RT-qPCR analysis. β-actin as an internal control. Prkaa2 (IUGR: 3.81 ± 0.97; AGA: 1.86 ± 0.84), Ppargc1a (IUGR: 8.72 ± 3.74; AGA: 4.21 ± 1.89), Gcgr (IUGR: 1.47 ± 0.39; AGA: 2.74 ± 1.55), Slc2a1 (IUGR: 1.84 ± 0.42;AGA: 1.38 ± 0.24), Rxrg (IUGR: 1.26 ± 0.15; AGA: 2.39 ± 0.56) and Acsl4 (IUGR: 5.18 ± 3.75; AGA: 3.50 ± 1.33) mRNA levels were analyzed in samples of IUGR, compared with AGA. Data indicate relative expression following normalization (*P < 0.05, **P < 0.01).