Autophagy Plays a Crucial Role in Ameloblast Differentiation

Tooth enamel is generated by ameloblasts. Any failure in amelogenesis results in defects in the enamel, a condition known as amelogenesis imperfecta. Here, we report that mice with deficient autophagy in epithelial-derived tissues (K14-Cre;Atg7^F/F and K14-Cre;Atg3^F/F conditional knockout mice) exhibit amelogenesis imperfecta. Micro-computed tomography imaging confirmed that enamel density and thickness were significantly reduced in the teeth of these mice. At the molecular level, ameloblast differentiation was compromised through ectopic accumulation and activation of NRF2, a specific substrate of autophagy. Through bioinformatic analyses, we identified Bcl11b, Dlx3, Klk4, Ltbp3, Nectin1, and Pax9 as candidate genes related to amelogenesis imperfecta and the NRF2-mediated pathway. To investigate the effects of the ectopic NRF2 pathway activation caused by the autophagy deficiency, we analyzed target gene expression and NRF2 binding to the promoter region of candidate target genes and found suppressed gene expression of Bcl11b, Dlx3, Klk4, and Nectin1 but not of Ltbp3 and Pax9. Taken together, our findings indicate that autophagy plays a crucial role in ameloblast differentiation and that its failure results in amelogenesis imperfecta through ectopic NRF2 activation.

Spatiotemporal MicroRNA-Gene Expression Network Related to Orofacial Clefts

Craniofacial structures change dynamically in morphology during development through the coordinated regulation of various cellular molecules. However, it remains unclear how these complex mechanisms are regulated in a spatiotemporal manner. Here we applied natural cubic splines to model gene and microRNA (miRNA) expression from embryonic day (E) 10.5 to E14.5 in the proximal and distal regions of the maxillary processes to identify spatiotemporal patterns of gene and miRNA expression, followed by constructing corresponding regulatory networks. Three major groups of differentially expressed genes (DEGs) were identified, including 3,927 temporal, 314 spatial, and 494 spatiotemporal DEGs. Unsupervised clustering further resolved these spatiotemporal DEGs into 8 clusters with distinct expression patterns. Interestingly, we found 2 clusters of differentially expressed miRNAs: 1 had 80 miRNAs monotonically decreasing and the other had 97 increasing across developmental stages. To evaluate the phenotypic relevance of these DEGs during craniofacial development, we integrated data from the CleftGeneDB database and constructed the regulatory networks of genes related to orofacial clefts. Our analysis revealed 2 hub miRNAs, mmu-miR-325-3p and mmu-miR-384-5p, that repressed cleft-related genes Adamts3, Runx2, Fgfr2, Acvr1, and Edn2, while their expression increased over time. On the contrary, 2 hub miRNAs, mmu-miR-218-5p and mmu-miR-338-5p, repressed cleft-related genes Pbx2, Ermp1, Snai1, Tbx2, and Bmi1, while their expression decreased over time. Our experiments indicated that these miRNA mimics significantly inhibited cell proliferation in mouse embryonic palatal mesenchymal (MEPM) cells and O9-1 cells through the regulation of genes associated with cleft palate and validated the role of our regulatory networks in orofacial clefts. To facilitate interactive exploration of these data, we developed a user-friendly web tool to visualize the gene and miRNA expression patterns across developmental stages, as well as the regulatory networks (https://fyan.shinyapps.io/facebase_shiny/). Taken together, our results provide a valuable resource that serves as a reference map for future research in craniofacial development.