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. 2020 Jan 7:8:e8196.
doi: 10.7717/peerj.8196. eCollection 2020.

DNA methylation profiling in recurrent miscarriage

Li Pi #  1 , Zhaofeng Zhang #  1 , Yan Gu  2 , Xinyue Wang  1 , Jianmei Wang  2 , Jianhua Xu  1 , Junwei Liu  1 , Xuan Zhang  1 , Jing Du  1
Affiliations

DNA methylation profiling in recurrent miscarriage

Li Pi et al. PeerJ. .

Abstract

Recurrent miscarriage (RM) is a complex clinical problem. However, specific diagnostic biomarkers and candidate regulatory targets have not yet been identified. To explore RM-related biological markers and processes, we performed a genome-wide DNA methylation analysis using the Illumina Infinium HumanMethylation450 array platform. Methylation variable positions and differentially methylated regions (DMRs) were selected using the Limma package in R language. Thereafter, gene ontology (GO) enrichment analysis and pathway enrichment analysis were performed on these DMRs. A total of 1,799 DMRs were filtered out between patients with RM and healthy pregnant women. The GO terms were mainly related to system development, plasma membrane part, and sequence-specific DNA binding, while the enriched pathways included cell adhesion molecules, type I diabetes mellitus, and ECM-receptor interactions. In addition, genes, including ABR, ALCAM, HLA-E, HLA-G, and ISG15, were obtained. These genes may be potential candidates for diagnostic biomarkers and possible regulatory targets in RM. We then detected the mRNA expression levels of the candidate genes. The mRNA expression levels of the candidate genes in the RM group were significantly higher than those in the control group. However, additional research is still required to confirm their potential roles in the occurrence of RM.

Keywords: DNA methylation profiling; Differentially methylated regions; Methylation variable positions; Quantitative real time PCR; Recurrent miscarriage.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Viewsof methylation variable positions (MVPs) result.
(A) Volcano plot of MVPs between control and RM. Each dot represents an individual MVPs. Dots that showed P values < = 0.01 after Benjamini & Hochberg correction are colored red while P values > = 0.01 after Benjamini & Hochberg correction are colored black. The x-axis represents log2 (Fold Change). The y-axis shows the −log10 (p value). (B) Heat map of MVPs between control and RM. Each row represents a locus and each column represents a sample. Red indicates that the methylation level is up-regulated and blue indicates down-regulated methylation level.
Figure 2
Figure 2. GO enrichment analysis and GO-Tree of differentially methylated regions (DMRs).
(A) GO enrichment analysis. The horizontal axis was the value of −log(P-value). The vertical axis was GO terms colored in three levels. The most significantly enriched terms for each level are shown. Red represented biological process (BP), green represented cellular component (CC), and blue represented molecular function (MF). (B–D) GO-tree. Hierarchical trees of three categories represented biological process (BP), cellular component (CC) and molecular function (MF) respectively. Colored squares represented the most enriched GO terms.
Figure 3
Figure 3. Pathway Enrichment Analysis of differentially methylated regions (DMRs).
KEGG pathway enrichment analysis. The horizontal axis was the top 20 most significantly enriched KEGG pathway. The vertical axis was the value of −log(P-value).
Figure 4
Figure 4. Comparison of mRNA expression levels of candidate genes in decidua tissues.
n = 3, mean ± SD, and independent sample T-test was used for comparison among groups.
See this image and copyright information in PMC

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