Dissertations and Theses (Open Access)
Graduation Date
Fall 2022
Degree Name
Doctor of Philosophy (PhD)
School Name
McWilliams School of Biomedical Informatics at UTHealth Houston
Advisory Committee
Zhongming Zhao, Ph.D.
Abstract
Cellular systems are regulated through dynamic and complex mechanisms that involve various signalling and gene regulations. Aberrant changes may disrupt the system, leading to disease development. Time-series data analysis includes the analysis of datasets from multiple time points, which is essential to capture dynamic gene expression and regulation. Although many efforts have been put into the time-series gene expression analysis, limited studies have explored both gene expression and regulation in a time-series manner. Our study proposed the first time specific informatics approach by integrating two critical regulators, TF and miRNA, and investigated their co-regulations. Specifically, we collected time-series bulk-tissue sequencing datasets covering mouse embryonic days (E) 10.5 to E14.5 and constructed novel regulatory networks between adjacent time points by assembling Feed-forward Loops (FFLs). We identified potential gene interactions that may influence orofacial development at different stages and conducted experimental validations of these networks in the related cell lines. Next, we applied the natural cubic spline model to better track the dynamic changes during the entire time course and identified eight clusters of genes with unique expression patterns. We then constructed pattern-specific regulatory networks by integrating data with CleftGeneDB, a database that manually curated orofacial cleft-related genes from literature. Lastly, to further resolve both gene expression and regulation dynamics at the cellular level, we simultaneously profiled single-cell transcriptomic and epigenomics from the mouse secondary palate across four developmental stages using novel single-cell multi-omic technology. Coupling differential gene expression vi analysis and motif enrichment analysis of positively linked peaks, we pinpointed a list of lineage-determining transcription factors, which were active at the early, middle, and late-stage of the trajectory, respectively. Collectively, our novel informatics approaches have advanced the understanding of molecular processes during orofacial development and the etiology of orofacial defects.
Recommended Citation
Yan, Fangfang, "Informatics Approaches for Capturing Dynamic Gene Expression and Regulation" (2022). Dissertations and Theses (Open Access). 97.
https://digitalcommons.library.tmc.edu/uthshis_dissertations/97
Keywords
Orofacial development, Gene expression regulation, Transcription factors, MicroRNAs, Time-series analysis, Single-cell multiomics, Regulatory networks, Orofacial clefts, Chromatin accessibility, Developmental trajectories