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Transcriptional Regulatory Networks of Craniofacial Development

颅面发育的转录调控网络

基本信息

批准号：
10633187
负责人：
Hong Li
金额：
$ 12.44万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10633187
关键词：
ATAC-seq Acceleration Adopted Affect Age Automobile Driving Bioinformatics Biological Assay Biological Models Biology Cartilage Cells ChIP-seq Complex Computational Science Computer Models Congenital Abnormality Data Data Science Data Set Databases Defect Dental Dependence Development Development Plans Ectoderm Embryo Enhancers Face FaceBase Faculty Foundations Future Gene Expression Genes Genetic Genetic Models Genetic Transcription Genome Genomics Goals Growth Histones Human Human Genetics Knowledge Laboratories Learning Live Birth Machine Learning Mentors Mentorship Mesenchymal Mesenchyme Methods Molecular Morphogenesis Multiomic Data Mus Muscle Mutant Strains Mice National Institute of Dental and Craniofacial Research Oral Pathology Pattern Performance Population Positioning Attribute Process Proteins Regulatory Element Research Resources Scientist Signal Pathway Signal Transduction Solid Study models System Techniques Technology Tissues Transcriptional Regulation Transgenic Organisms Validation bone career career development cell type craniofacial craniofacial development critical period design differential expression in silico interest medical specialties mouse model network models next generation orofacial cleft programs promoter public database recruit single-cell RNA sequencing spatiotemporal tenure track transcription factor transcription regulatory network transcriptome transcriptome sequencing

项目摘要

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.

摘要