Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, USA.
J Dent Res. 2022 Oct;101(11):1398-1407. doi: 10.1177/00220345221105816. Epub 2022 Jun 30.
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 , , , , and , 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 , , , , and , 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.
颅面结构在发育过程中通过各种细胞分子的协调调节在形态上发生动态变化。然而,这些复杂机制如何在时空上进行调节仍不清楚。在这里,我们应用自然三次样条来模拟从胚胎第 10.5 天到第 14.5 天上颌突的近侧和远侧区域的基因和 microRNA(miRNA)表达,以鉴定基因和 miRNA 表达的时空模式,然后构建相应的调控网络。鉴定出三个主要的差异表达基因(DEG)组,包括 3927 个时间 DEG、314 个空间 DEG 和 494 个时空 DEG。无监督聚类进一步将这些时空 DEG 分为 8 个具有不同表达模式的聚类。有趣的是,我们发现了 2 个差异表达 miRNA 簇:1 个簇中有 80 个 miRNA 随时间单调下降,另一个簇中有 97 个 miRNA 随时间增加。为了评估这些 DEG 在颅面发育过程中的表型相关性,我们整合了 CleftGeneDB 数据库的数据,并构建了与口腔颌面裂相关基因的调控网络。我们的分析揭示了 2 个枢纽 miRNA,mmu-miR-325-3p 和 mmu-miR-384-5p,它们抑制了与裂相关的基因,而它们的表达随时间增加。相反,2 个枢纽 miRNA,mmu-miR-218-5p 和 mmu-miR-338-5p,抑制了与裂相关的基因,而它们的表达随时间减少。我们的实验表明,这些 miRNA 模拟物通过调节与腭裂相关的基因显著抑制了小鼠胚胎腭中胚层(MEPM)细胞和 O9-1 细胞的增殖,并验证了我们的调控网络在口腔颌面裂中的作用。为了便于对这些数据进行交互式探索,我们开发了一个用户友好的网络工具,用于可视化基因和 miRNA 表达模式在发育阶段以及调控网络(https://fyan.shinyapps.io/facebase_shiny/)。总之,我们的结果提供了一个有价值的资源,为未来的颅面发育研究提供了参考图谱。
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