Promoter architecture and gene expression dynamics in embryonic development

Abstract

Genes indispensable for proper embryonic development show intricate patterns of expression throughout the time, space and magnitude of their activity. This diversity is enabled by elaborate regulatory mechanisms that guide their expression. They also possess a distinct type of core promoters that enable the integration of all regulatory inputs. However, it is still not clear how is coordination of regulation achieved. The first step towards understanding this process is to characterise dynamics of expression, and core promoter features that process the regulation. In this thesis, I explored the diversity of spatio-temporal gene expression during zebrafish development. I defined a novel measure of anatomical specificity that defines how precisely an anatomical structure is defined in the Anatomical Ontology system. Using anatomical specificity measure, I quantified gene expression dynamics from mRNA in situ hybridisation data. Gene expression divergence from in situs was used to predict expression levels from RNA-seq expression data. This analysis allowed me to propose a measure of gene expression complexity which showed that genes with the highest complexity score are developmental genes, whereas genes with low complexity score are involved in housekeeping functions. Next, I developed a method that reports significantly enriched core promoter elements in a group of genes. Using this method, I compared differences in core promoter composition in active genes expressed in different developmental periods. In addition, this method found groups of genes with a specific core promoter structure that are specified for a biological process. Finally, I used scRNA-seq data from zebrafish development to identify patterns of gene co-expression across different cell clusters. Co-expression suggests that a gene pair possesses a common regulatory programme. I show that genes with the most divergent co-expression patterns across development are developmental genes and that housekeeping genes have least diverse co-expression patterns. I went further to create co-expression networks which allowed me to analyse co-expression patterns into more details.