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. 2016 Sep;1859(9):1228-1237.
doi: 10.1016/j.bbagrm.2016.04.002. Epub 2016 Apr 11.

Bioinformatic analysis of microRNA networks following the activation of the constitutive androstane receptor (CAR) in mouse liver

Ruixin Hao  1 , Shengzhong Su  2 , Yinan Wan  3 , Frank Shen  4 , Ben Niu  2 , Denise M Coslo  2 , Istvan Albert  3 , Xing Han  5 , Curtis J Omiecinski  6
Affiliations

Bioinformatic analysis of microRNA networks following the activation of the constitutive androstane receptor (CAR) in mouse liver

Ruixin Hao et al. Biochim Biophys Acta. 2016 Sep.

Abstract

The constitutive androstane receptor (CAR; NR1I3) is a member of the nuclear receptor superfamily that functions as a xenosensor, serving to regulate xenobiotic detoxification, lipid homeostasis and energy metabolism. CAR activation is also a key contributor to the development of chemical hepatocarcinogenesis in mice. The underlying pathways affected by CAR in these processes are complex and not fully elucidated. MicroRNAs (miRNAs) have emerged as critical modulators of gene expression and appear to impact many cellular pathways, including those involved in chemical detoxification and liver tumor development. In this study, we used deep sequencing approaches with an Illumina HiSeq platform to differentially profile microRNA expression patterns in livers from wild type C57BL/6J mice following CAR activation with the mouse CAR-specific ligand activator, 1,4-bis-[2-(3,5,-dichloropyridyloxy)] benzene (TCPOBOP). Bioinformatic analyses and pathway evaluations were performed leading to the identification of 51 miRNAs whose expression levels were significantly altered by TCPOBOP treatment, including mmu-miR-802-5p and miR-485-3p. Ingenuity Pathway Analysis of the differentially expressed microRNAs revealed altered effector pathways, including those involved in liver cell growth and proliferation. A functional network among CAR _targeted genes and the affected microRNAs was constructed to illustrate how CAR modulation of microRNA expression may potentially mediate its biological role in mouse hepatocyte proliferation. This article is part of a Special Issue entitled: Xenobiotic nuclear receptors: New Tricks for An Old Dog, edited by Dr. Wen Xie.

Keywords: Constitutive androstane receptor; Liver cancer; Mouse; TCPOBOP; microRNA.

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Figures

Figure 1
Figure 1
Scheme of the experimental workflow. Mice were treated with TCPOBOP, and liver tissues were harvested, followed by total RNA extraction. RNA was sequenced on the Illumina HiSeq 2500 platform. Sequencing data were processed using miRDeep2 [29], and differentially expressed miRNAs were determined using DESeq2 package of Bioconductor R. Selected sequencing data were validated using RT-qPCR. The differentially expressed miRNAs were exported into IPA for pathway analysis and integrative analysis with a transcriptome dataset (GSE13688) [45].
Figure 2
Figure 2
Scatterplot matrix of the miRNA-seq data. As a quality check, a scatterplot matrix of the miRNA expression datasets exported from miRDeep2 was produced with the R hexbin package. Examining the pairwise correlation between all treatment groups, the scatterplot matrix shows all the pairwise scatter plots of the log2-transformed miRNA expression values from the 3 vehicle (DMSO) and 3 TCPOBOP treatment groups. Overall highly positive correlations were observed between all treatment groups, indicating that the raw sequence data were of high quality.
Figure 3
Figure 3
CAR activation by TCPOBOP altered miRNA profiling. (A) PCA plot of normalized expression values of miRNAs. PCA analysis was performed using R Bioconductor DESeq2. A clear separation between the DMSO and TCPOBOP treatment samples was observed. (B) Hierarchical clustering analysis of differentially expressed miRNAs. A heatmap was produced using the pheatmap function from the pheatmap package in R. Differentially expressed miRNAs shown at the rows of the heatmap were clustered using Euclidean distance and complete linkage. The treatment groups shown in the columns were clustered using correlation distance and complete linkage. The color scale at the top-right corner illustrates the expression value of a particular miRNA on a log2 scale.
Figure 3
Figure 3
CAR activation by TCPOBOP altered miRNA profiling. (A) PCA plot of normalized expression values of miRNAs. PCA analysis was performed using R Bioconductor DESeq2. A clear separation between the DMSO and TCPOBOP treatment samples was observed. (B) Hierarchical clustering analysis of differentially expressed miRNAs. A heatmap was produced using the pheatmap function from the pheatmap package in R. Differentially expressed miRNAs shown at the rows of the heatmap were clustered using Euclidean distance and complete linkage. The treatment groups shown in the columns were clustered using correlation distance and complete linkage. The color scale at the top-right corner illustrates the expression value of a particular miRNA on a log2 scale.
Figure 4
Figure 4
CAR _targeted miRNAs. (A) Selected miRNAs were validated by RT-qPCR. Expression of levels of miR-182-5p, miR-802-3p and miR-203-5p showed results consistent with the derived RNA-seq data; **p<0.05. Though the trends of the expression levels of miR-122-5p and mi-183-5p agreed with the differential expression patterns ascertained by the RNA-seq analyses, these changes were not statistically significant RT-qPCR at the p<0.05 level. (B) CAR _targeted miRNA network built by IPA. The network indicates that CAR regulates several miRNAs through MYC, ESR1, NO0B2, PPARa, and CEBPA.
Figure 4
Figure 4
CAR _targeted miRNAs. (A) Selected miRNAs were validated by RT-qPCR. Expression of levels of miR-182-5p, miR-802-3p and miR-203-5p showed results consistent with the derived RNA-seq data; **p<0.05. Though the trends of the expression levels of miR-122-5p and mi-183-5p agreed with the differential expression patterns ascertained by the RNA-seq analyses, these changes were not statistically significant RT-qPCR at the p<0.05 level. (B) CAR _targeted miRNA network built by IPA. The network indicates that CAR regulates several miRNAs through MYC, ESR1, NO0B2, PPARa, and CEBPA.
Figure 5
Figure 5
CAR _targeted miRNA and mRNA in liver cancer. Paired CAR _targeted miRNAs and mRNAs were pooled for IPA pathway analysis. The “liver cancer” category was significantly enriched from multiple miRNAs and mRNAs. Network shapes are shown in the figure inset. Red labels represent up-regulated miRNAs or mRNAs. Green labels represent down-regulated miRNAs or mRNAs.
Figure 6
Figure 6
Putative miRNA:mRNA interaction upon activation of CAR by TCPOBOP in liver cancer. Paired miRNAs and mRNAs enriched in the “liver cancer” category were imported inIPA to assess the interactions among these parameters. Connections were established by IPA based on data mining or prediction according to miRNA seed sequences. Network shapes and relationships are shown in the figure inset. Red colors represent miRNAs while green colors represent mRNAs.
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