Transcriptional Regulatory Networks of Craniofacial Development

摘要 人类颅面发育是一个复杂的过程，经常出错会导致一类主要的问题 出生缺陷，即口颌面裂，影响大约七百个活产儿中就有一个。 Proper facial development in 小鼠和人类需要三组成对的面部突起随着生长而聚集在一起， morphogenesis, and fusion.人类和小鼠的胚胎面部发育惊人相似，这使得 小鼠是人类可用的最佳模型系统。先前的研究表明，表达 数千个基因在组织层、年龄和/或显着性以及细胞群中发生变化 during early mouse facial development.然而，我们对于这些是如何实现的仍然只有初步的了解。 变化是通过发育中的转录调节剂的相互作用来调节的。要了解 这项研究试图构建面部发育过程中基因如何转录调控的机制 通过对公开可用的多基因进行计算机分析，以时空方式构建转录调控网络 组学数据集。目标 1 将重点关注转录调控网络的识别和验证 在面部间充质中进行操作，重点是超级增强剂。 Aim 2 will adopt a similar approach to study 外胚层作为间充质的重要信号中心。 Finally, in Aim 3 I will apply 目标 1 和 2 的知识在单细胞水平上构建转录调控网络。这些目标 将利用可用的 RNA-seq、ATAC-seq、组蛋白标记 ChIP-seq、转录因子 ChIP-seq、 来自野生型或突变小鼠的大量和单细胞 RNA-seq 数据，以及面部增强子表达 数据库。这些研究的完成将预测基因如何在生物体中进行转录调控。 面部发育过程中的时空方式，并发现一组核心转录因子和超 enhancers controlling facial development.这些转录调控网络将与 人类口颌面裂的遗传和分子基础，并将提供明确的可测试预测 关于转录因子的功能和异常表达的后果。性能和 实现这些目标也将成为我职业发展计划的一个重要组成部分，其中我的 目标是获得独立的终身教授职位，并作为下一代的导师 科学家。我职业发展计划的一个主要方面是加强我在生物信息学方面不断增长的实力 通过学习该专业的更先进技术以及基于新计算的方法，例如 作为机器学习。在这方面，我的目标和职业发展计划与《通知》一致 NIDCR 对利用生物信息学支持牙科、口腔和颅面研究的特别兴趣 (NOSI) 此应用程序针对的计算和数据科学方法 (NOT-DE-20-006)。我有 招募了一支颅面生物学、生物信息学、机器学习等专业的导师团队 职业发展帮助我实现这些目标。

Abstract Human craniofacial development is a complex process and frequently goes awry to cause a major class of birth defects, orofacial clefting, which affects approximately 1 in 700 live births. Proper facial development in mouse and human requires three sets of paired facial prominences coming together by growth, morphogenesis, and fusion. Embryonic facial development is strikingly similar in human and mouse, making the mouse the best available model system for human. Previous studies have shown that the expression of many thousands of genes changes across tissue layer, age, and/or prominence, as well as cell population during early mouse facial development. However, we still only have a rudimentary understanding of how these changes are regulated by the interaction of transcriptional modulators in the developing face. To understand how genes are transcriptionally regulated during facial development, this research seeks to construct transcriptional regulatory networks in a temporospatial manner by in silico analysis of publicly available multi- omic datasets. Aim 1 will focus on the identification and verification of transcriptional regulatory networks operating in facial mesenchyme with a focus on super-enhancers. Aim 2 will adopt a similar approach to study the ectoderm which acts as a vital signaling center for the mesenchyme. Finally, in Aim 3 I will apply knowledge from Aims 1 and 2 to build transcriptional regulatory networks at the single cell level. These aims will take advantage of available RNA-seq, ATAC-seq, histone marker ChIP-seq, transcription factor ChIP-seq, bulk and single cell RNA-seq data from wild-type or mutant mice, as well as facial enhancer expression databases. Accomplishment of these studies will predict how genes are transcriptionally regulated in a temporospatial manner during facial development and discover sets of core transcription factors and super- enhancers controlling facial development. These transcriptional regulatory networks will be relevant to the genetic and molecular underpinnings of human orofacial clefting, and will provide clear testable predictions about transcription factor function and the consequences of aberrant expression. Performance and accomplishment of these Aims will also act as a major component of my career development plan, in which my goal is to obtain and independent tenure-track faculty position and serve as a mentor to the next generation of scientists. A major aspect of my career development plan is to build on my growing strength in bioinformatics by learning more advanced techniques in this specialty alongside new computational based approaches, such as machine learning. In this respect, my Aims and career development plan are aligned with a Notice of Special Interest (NOSI) of NIDCR in Supporting Dental, Oral, and Craniofacial Research Using Bioinformatic, Computational, and Data Science Approaches (NOT-DE-20-006) for which this application is targeted. I have recruited a mentorship team with specialties in craniofacial biology, bioinformatics, machine learning, and career development to help me achieve these goals.