Transcriptional Regulatory Networks of Craniofacial Development

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.