APPROACHES FOR IDENTIFICATION OF TRANSCRIPTIONAL AND POST-TRANSCRIPTIONAL REGULATORS OF MESENCHYMAL STEM CELL DIFFERENTIATION USING TIME-SERIES EPIGENOMIC DATA

Gene regulatory networks (GRNs) control cellular differentiation and development and recapitulate the physical interactions between transcription factors (TFs) and their influence on their target genes that ultimately results into a defined cell phenotype. In addition, cellular differentiation represents the path a cell undergoes through multiple stages before reaching a terminally differentiated state and is by nature dynamic. Moreover, epigenetic regulation as well as post-transcriptional control of gene expression are critical for faithful cellular phenotype. Cellular differentiation of progenitor cells into their daughter cells provide a dynamic controllable system to study the epigenetic mechanisms as well as the transcriptional output that take place towards cellular specifications, and the TFs and non-coding RNAs that dictate their differentiation. Here, we have generated time-series transcriptomic and epigenomic data during the differentiation of bone marrow stromal cells towards adipocytes and osteoblasts and characterized a novel approach called EPIC-DREM to construct dynamic GRNs of adipocytes and osteoblasts. In order to focus on shared transcriptional regulators of early commitment of bone marrow stromal cells towards adipocytes and osteoblasts, we have concentrated our analysis on dynamic super-enhancers to prioritize the identified TFs and discovered aryl hydrocarbon receptor (AHR) as a transcriptional regulator of the multipotent state. In addition, the generated of time-series epigenomic data were used as input for linear regression analysis that allowed to predict genes that are dynamically controlled by post-transcriptional regulators such as microRNAs (miRs). Indeed, genes that differ from their predicted expression level as assessed by the residuals of the linear regression model can be informative about their mRNA stability. In order to decipher genes that are under dynamic post-transcriptional control, the standard deviation of gene’s residuals was taken as a dynamic measure of changes in mRNA stability and clustering analysis coupled to microRNA motifs enrichment analysis allowed to identify post-transcriptionally co-regulated mRNAs. Based on the linear regressions analysis, miR-204 was identified as a potential regulator of adipogenesis. Integration of these types of data can contribute to the understanding of transcriptional and post-transcriptional control of cell differentiation and the here established approaches for key regulators identification can be widely applied to study other cell states transitions.