Identification of gene specific cis-regulatory elements during differentiation of mouse embryonic stem cells: An integrative approach using high-throughput datasets

Gene expression governs cell fate, and is regulated via a complex interplay of transcription factors and molecules that change chromatin structure. Advances in sequencing-based assays have enabled investigation of these processes genome-wide, leading to large datasets that combine information on the dynamics of gene expression, transcription factor binding and chromatin structure as cells differentiate. While numerous studies focus on the effects of these features on broader gene regulation, less work has been done on the mechanisms of gene-specific transcriptional control. In this study, we have focussed on the latter by integrating gene expression data for the in vitro differentiation of murine ES cells to macrophages and cardiomyocytes, with dynamic data on chromatin structure, epigenetics and transcription factor binding. Combining a novel strategy to identify communities of related control elements with a penalized regression approach, we developed individual models to identify the potential control elements predictive of the expression of each gene. Our models were compared to an existing method and evaluated using the existing literature and new experimental data from embryonic stem cell differentiation reporter assays. Our method is able to identify transcriptional control elements in a gene specific manner that reflect known regulatory relationships and to generate useful hypotheses for further testing.Author summary: The inherited information in our DNA genomes is a code which defines both the functional units (proteins, nucleic acids etc.), and patterns of their usage, necessary to make life. The genome in mammals, such as man and mouse, has genes which code for about 20000 different proteins, but the usage of these proteins differs in each different type of cell within these complex multicellular organisms. How this differential usage is controlled in known as genetic regulation, and that is what we study here. We know that the details lie in how genes are turned on and off, but until the advent of high-throughput sequencing technology a genome-wide study was nearly impossible. Further complicating our efforts to understand genetic regulation is the involvement of parts of the genome that were previously deemed junk. In this work, we have focussed on how the genes are controlled at various developmental stages in mouse, by looking at the sequencing data from different regulatory mechanisms such as protein binding and local changes to DNA packaging etc. On a gene-by-gene basis, we have built statistical models that predict how genes are controlled when cells develop. These predictions provide a focus for future experimental studies of genetic regulation.