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.

通过各种细胞分子的协调调节，颅面结构在发育过程中形态发生动态变化。然而，目前尚不清楚这些复杂的机制如何以时空方式进行调节。在这里，我们应用自然三次样条来模拟上颌突近端和远端区域从胚胎日 (E) 10.5 到 E14.5 的基因和 microRNA (miRNA) 表达，以确定基因和 miRNA 表达的时空模式，然后构建相应的模型。监管网络。确定了三大类差异表达基因 (DEG)，包括 3,927 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 分解为 8 个具有不同表达模式的簇。有趣的是，我们发现了 2 个差异表达 miRNA 的簇：其中 1 个有 80 个单调递减的 miRNA，另一个有 97 个在各个发育阶段增加的 miRNA。为了评估这些差异表达基因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，并构建了与口面裂相关的基因的调控网络。我们的分析揭示了 2 个中枢 miRNA，mmu-miR-325-3p 和 mmu-miR-384-5p，它们抑制裂口相关基因Adamts3 、 Runx2 、 Fgfr2 、 Acvr1和Edn2 ，而它们的表达随着时间的推移而增加。相反，2个中枢miRNA，mmu-miR-218-5p和mmu-miR-338-5p，抑制裂口相关基因Pbx2 、 Ermp1 、 Snai1 、 Tbx2和Bmi1 ，而它们的表达随着时间的推移而下降。 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，显着抑制小鼠胚胎腭间充质 (MEPM) 细胞和 O9-1 细胞的细胞增殖，并验证了我们的调控网络在口颌裂中的作用。为了促进这些数据的交互式探索，我们开发了一个用户友好的网络工具来可视化整个发育阶段的基因和 miRNA 表达模式以及调控网络 (https://fyan.shinyapps.io/facebase_shiny/)。总而言之，我们的结果提供了宝贵的资源，可以作为未来颅面发育研究的参考图。