Transcriptome and Network Analysis of Cleft Palate

腭裂的转录组和网络分析

• 批准号: 10539242
• 负责人: Ethylin Wang Jabs
• 金额: $ 79.34万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2023-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10539242
• 关键词: Adhesions; Affect; Age; Anterior; Binding; Biological; Biomechanics; CRISPR/Cas technology; Cells; Cleft Palate; Clinical; Complex; Computational Biology; Congenital Abnormality; Critical Pathways; Data; Data Set; Defect; Development; Developmental Biology; Diagnosis; Disease; Embryo; Epithelium; Etiology; Event; FGFR2 gene; Fibroblast Growth Factor; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Genetic; Genetic Transcription; Genotype; Goals; Growth; Human; Immunohistochemistry; In Situ Hybridization; Individual; Internet; Intervention; Intuition; Knock-in; Knockout Mice; Libraries; Link; Literature; Maps; Maxilla; Medial; Mediating; Medical; Mesenchymal; Mesenchyme; Methods; Modeling; Molecular; Morphogenesis; Morphology; Movement; Multiomic Data; Mus; Mutant Strains Mice; Mutation; Operative Surgical Procedures; Organ Culture Techniques; Outcome; Palate; Pathologic; Pathway Analysis; Pathway interactions; Pattern; Population; Prevention; Process; Property; Psychosocial Assessment and Care; RNA; RNA Splicing; Resolution; Role; SHH gene; Series; Signal Pathway; Signal Transduction; Structural Congenital Anomalies; Structure; System; Systems Biology; Technology; Tissue-Specific Gene Expression; Tissues; Validation; Wild Type Mouse; Work; advanced system; cell type; craniofacial; differential expression; gene discovery; human model; improved; in silico; in vivo; innovation; insight; molecular scale; mouse model; mutant; mutant mouse model; network models; novel; null mutation; oral cleft; orofacial cleft; palatal shelves; palatogenesis; prevent; psychosocial; simulation; single cell sequencing; single-cell RNA sequencing; tool; transcription factor; transcriptome; transcriptome sequencing; transcriptomics

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.

摘要 腭裂是最常见的结构性出生缺陷之一。手术矫正以及医疗和心理社会 护理给个人和社会带来了沉重的负担。提高对腭裂病因学的认识 可能会导致诊断和治疗的改进。腭发生是非常复杂的。配对 腭架首先从上颌突垂直延伸，必须充分生长， 水平高度，它们的内侧边缘接触。覆盖在内侧骨架上的上皮细胞解体， 允许它们融合。所涉及的关键过程包括上皮/间充质相互作用和转变。 对人类和小鼠的研究已经确定了至少429个与口腔裂相关的基因。还原论者 科学方法已经提供了关于腭发育中的单个基因和途径的细节，但是直观的 所产生的模型不足以表示该过程的巨大复杂性。我们会委聘 转录组和网络分析，以了解生物成分如何共同工作，以产生系统- 上皮分化和粘附的广泛结果，间充质生物力学特性影响 上皮和间充质的前/后区域化。这些 这些过程需要多个交叉调节信号通路的整合。成纤维细胞生长因子 和sonic hedgehog（SHH）是两条这样的通路，它们的信息通过以下途径与其他通路整合： 转录因子p63。我们将从腭架中生成批量和单细胞RNA-seq库 野生型小鼠，以发现基因共表达和调控网络，以及参与 正常的腭发育。对于批量RNA-seq文库，我们将分离前上皮和后上皮， 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 四种突变小鼠品系的方法，利用这些基因扰动来识别关键驱动基因， 在这些网络中相互作用的路径。我们将研究两种激活FGFR 2突变， 上皮或间充质（S252 W或C342 Y）的差异效应以及SHH和 p63，在上皮中表达。互补的批量和单细胞RNA-seq文库将鉴定差异表达。 基因表达和新的关键组成部分和途径的关键腭。我们将使用这些数据集， 结合公开可获得味觉相关数据集来构建高分辨率、多尺度的分子 这些网络将用于开发腭发育的预测性机械模型。新型分子网络 通过多尺度网络建模方法确定的关键调控因子将通过现场验证 杂交、免疫组织化学、腭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， 正常和异常腭发育的网络模型将对临床，颅面， - 组学和发育生物学领域。