Overexpression of miR-1306-5p, miR-3195, and miR-3914 Inhibits Ameloblast Differentiation through Suppression of Genes Associated with Human Amelogenesis Imperfecta

Abstract

Amelogenesis imperfecta is a congenital form of enamel hypoplasia. Although a number of genetic mutations have been reported in humans, the regulatory network of these genes remains mostly unclear. To identify signatures of biological pathways in amelogenesis imperfecta, we conducted bioinformatic analyses on genes associated with the condition in humans. Through an extensive search of the main biomedical databases, we found 56 genes in which mutations and/or association/linkage were reported in individuals with amelogenesis imperfecta. These candidate genes were further grouped by function, pathway, protein-protein interaction, and tissue-specific expression patterns using various bioinformatic tools. The bioinformatic analyses highlighted a group of genes essential for extracellular matrix formation. Furthermore, advanced bioinformatic analyses for microRNAs (miRNAs), which are short non-coding RNAs that suppress target genes at the post-transcriptional level, predicted 37 candidates that may be involved in amelogenesis imperfecta. To validate the miRNA-gene regulation association, we analyzed the target gene expression of the top seven candidate miRNAs: miR-3195, miR-382-5p, miR-1306-5p, miR-4683, miR-6716-3p, miR-3914, and miR-3935. Among them, miR-1306-5p, miR-3195, and miR-3914 were confirmed to regulate ameloblast differentiation through the regulation of genes associated with amelogenesis imperfecta in AM-1 cells, a human ameloblastoma cell line. Taken together, our study suggests a potential role for miRNAs in amelogenesis imperfecta.

Keywords: amelogenesis imperfecta; enamel formation; microRNAs; tooth development; tooth formation.

Figure 1

The flowchart for literature mining based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, including the sequential steps for the identification, screening, eligibility check, and qualification of the literature.

Figure 2

Bioinformatic analysis of the identified amelogenesis imperfecta-related genes, including functional enrichment analysis, construction of amelogenesis imperfecta-related protein interaction network, tissue-specific expression of amelogenesis imperfecta-related genes, and construction of miRNA–gene regulations.

Figure 3

Functional enrichment analysis of amelogenesis imperfecta-related genes. (a) Top 20 most significantly enriched diseases. (b) Enriched pathway–gene network: nodes annotated in black represent pathways, while the others in red denote genes. Genes associated with multiple terms, especially multiple categories of functions, are marked with different colors.

Figure 4

Protein–protein interaction (PPI) network of amelogenesis imperfecta-related genes. Red nodes represent amelogenesis imperfecta-related genes and blue nodes denote the genes that interact with amelogenesis imperfecta genes in the PPI network. The size of the nodes was ranked according to node degree. The PPI network we used here was a combination of all five databases.

Figure 5

Characteristics of miRNAs associated with amelogenesis imperfecta (AI). (a) The distribution of miRNAs interacting with amelogenesis imperfecta-associated genes. (b) The enriched miRNA regulation network. Red circles denote genes related with amelogenesis imperfecta. Green squares denote human miRNAs enriched with amelogenesis imperfecta-related genes. An edge is laid when interaction between miRNA and gene has been reported in the database.

Figure 6

Effects of overexpression of candidate miRNAs on ameloblast differentiation. (a) Scheme of the experimental timeline. (b) Gene expression of AMELX, AMTN, KLK4, and MMP20 after treatment with a mimic of control or candidate miRNAs in AM-1 cells. * p < 0.05; ** p < 0.01, *** p < 0.001. (c) Immunocytochemistry analysis for KLK4 (green) in AM-1 cells under the indicated conditions. The nuclei were counterstained with 4’,6’-diamidino-2-phenylindole [DAPI (blue)]. Scale bar, 150 μm.

Figure 7

Effects of overexpression of candidate miRNAs on target gene expression. (a) Quantitative real-time polymerase chain reaction (RT-PCR) analyses for the target gene expression after treatment with control and miR-1306-5p mimic. * p < 0.05; ** p < 0.01; NS, not significant. Control miR proliferation: cells were treated with control miR mimic during the cell proliferation phase. Control miR differentiation: cells were treated with control miR mimic under differentiation conditions for 3 days. (b) Quantitative RT-PCR analyses for target gene expression after treatment with control and miR-3195 mimic. * p < 0.05; ** p < 0.01; NS, not significant. (c) Quantitative RT-PCR analyses for target gene expression after treatment with control and miR-3914 mimic. * p < 0.05; ** p < 0.01; NS, not significant.