Transcriptional enhancers represent the primary basis for differential gene expression. These elements regulate cell type specificity, development, and evolution, with many human diseases resulting from altered enhancer activity. To date, a key gap in our knowledge is how enhancers select specific promoters for activation.
To fill this gap, in this thesis, I first developed an Integrated Method for Predicting Enhancer Targets (IM-PET). Leveraging abundant “omics” data, I devised and characterized multiple genomic features for distinguishing true enhancer-promoter (EP) pairs from non-interacting pairs. I integrated these features into a probabilistic predictor for EP interactions. Multiple validation experiments demonstrated a significant improvement over extent state-of-the-art approaches. Systematic analyses of EP interactions across twelve human cell types reveals global features of EP interactions.
Second, we used a well-established viral infection model to map the dynamic changes of enhancers and super-enhancers during the CD8+ T cell responses. Our analysis illustrated the complexity and dynamics of the underlying EP interactome during cell differentiation. Taking advantage of the predicted EP interactions, we constructed stage-specific transcriptional regulatory networks, which is critical for understanding the regulatory mechanism during CD8+ T cell differentiation.
Third, recent progress in mapping technologies for chromatin interactions has led to a rapid increase in this type of interaction data. However, there is a lack of a comprehensive depository for chromatin interactions identified by all major technologies. To address this problem, we have developed the 4DGenome database through comprehensive literature curation of experimentally derived interactions. We envision a wide range of investigations will benefit from this carefully curated database.