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.