Transcriptome and Network Analysis of Cleft Palate

摘要 腭裂是最常见的结构性出生缺陷之一。外科矫正与医学和心理社会 护理给个人和社会带来了沉重的负担。提高对腭裂病因的认识 潜在地将导致诊断和治疗的改进。腭裂的发生极其复杂。成对的 腭架首先从上颌突垂直延伸，必须充分生长，以便在 水平高度它们的内侧边缘相接。覆盖内侧货架的上皮细胞解体， 使它们能够融合。涉及的关键过程包括上皮/间充质的相互作用和过渡。 在人类和老鼠身上的研究已经确定了至少429个与口腔唇裂有关的基因。简化论者 科学方法已经提供了关于腭裂形成中单个基因和途径的详细信息，但直观的 所生成的模型不足以表示该过程的巨大复杂性。我们将聘用 转录组和网络分析以了解生物成分如何协同工作来产生系统- 上皮分化和黏附的广泛结局，间充质生物力学特性的影响 上皮细胞和间充质的重塑和架子抬高，以及前后区域化。这些 这一过程需要整合多条交叉调节的信号通路。成纤维细胞生长因子 和Sonic Hedgehog(SHH)是两条这样的通路，它们的信息通过与其他通路整合 转录因子p63。我们将从Palal货架上生成批量和单细胞rna-seq文库 野生型小鼠发现基因共表达和调控网络，以及参与的特定细胞群 正常的腭部发育。对于成批的rna-seq文库，我们将分离前、后上皮和 间充质隔室允许对转录变化进行区域特异性分析。我们将使用相同的 四个突变小鼠系的方法，利用这些基因扰动来识别关键驱动基因和 在这些网络中相互作用的路径。我们将研究两个激活的FGFR2突变，它们发挥其 S252W或C342Y上皮细胞和间充质细胞的差异效应及SHH和C342Y的零突变 P63，表达于上皮细胞。互补散装和单细胞rna-seq文库将识别差异 对腭裂发生至关重要的基因表达和新的关键成分和途径。我们将使用这些数据集， 结合公开可用的与腭部相关的数据集来构建高分辨率、多尺度的分子 这些网络将被用来开发具有预测性的、机械性的腭裂形成模型。新型分子网络 通过多尺度网络建模方法确定的关键监管机构将通过现场验证 杂交、免疫组织化学、腭部器官培养和小鼠模型。我们的创新方法 生成全面的数据集，使用先进的系统生物学技术，并建立多尺度 腭突发育正常和异常的网络模型将对临床、颅面、 --组学、发育生物学领域。

ABSTRACT Cleft palate is one of the most common structural birth defects. Surgical correction and medical and psychosocial care impose significant personal and societal burdens. Increased understanding of the etiology of cleft palate potentially will lead to improvements in diagnosis and treatment. Palatogenesis is enormously complex. Paired palatal shelves first extend vertically from the maxillary processes and must grow sufficiently so that upon horizontal elevation their medial edges come into contact. Epithelia covering the medial shelves disintegrate, allowing their fusion. Key processes involved include epithelial/mesenchymal interactions and transitions. Studies in humans and mice have identified at least 429 genes associated with oral clefting. Reductionist scientific approaches have provided detail about individual genes and pathways in palatogenesis, but the intuitive models generated are not sufficient to represent the enormous complexity of the process. We will employ transcriptome and network analyses to understand how biological components work together to produce system- wide outcomes of epithelial differentiation and adhesion, mesenchymal biomechanical properties affecting remodeling and shelf elevation, and anterior/posterior regionalization of epithelia and mesenchyme. These processes require the integration of multiple cross-regulating signaling pathways. Fibroblast growth factor (FGF) and sonic hedgehog (SHH) are two such pathways, and their information is integrated with other pathways by the transcription factor p63. We will generate bulk and single-cell RNA-seq libraries from the palatal shelves of wild type mice to discover gene coexpression and regulatory networks, and specific cell populations involved in normal palatogenesis. For bulk RNA-seq libraries we will separate anterior and posterior epithelial and mesenchymal compartments allowing region-specific analysis of transcriptional changes. We will use the same approach for four mutant mouse lines, exploiting these gene perturbations to identify key driver genes and interacting pathways within these networks. We will study two activating FGFR2 mutations that exert their differential effects from the epithelium or the mesenchyme (S252W or C342Y) and null mutations of SHH and p63, expressed in the epithelium. Complementary bulk and single-cell RNA-seq libraries will identify differential gene expression and novel key components and pathways critical to palatogenesis. We will use these datasets, in conjunction with publicly available palate-related datasets to build high-resolution, multiscale molecular networks that will be used to develop predictive, mechanistic models of palatogenesis. Novel molecular networks and key regulators identified through the multiscale network modeling approach will be validated by in situ hybridization, immunohistochemistry, palatal organ cultures, and mouse models. Our innovative approach to generating comprehensive datasets, using advanced systems biology technologies, and building multiscale network models of normal and abnormal palatogenesis will have a large impact on the clinical, craniofacial, -omics, and developmental biology fields.