ena-STUDY-Life Sciences Research Unit-18-05-2017-15:28:30:933-69
Temporal epigenomic and transcriptomic profiling of mesenchymal differentiation
We have generated time-series transcriptomic and epigenomic data             during the differentiation of bone marrow adipocytes and osteoblasts from             their shared mesenchymal precursor cells using RNA-seq and ChIP-seq for several histone modifications,              in order to identify the gene regulatory networks underlying these differentiation processes,              and to better understand their dynamics over time. RNA and chromatin were collected              at 6 different time points during both of the 15-day differentiation processes and active enhancers (H3K27ac),              promoters (H3K4me3) and transcribed regions (H3K36me3) were mapped in both lineages.              The identified time point-specific open chromatin regions were annotated for transcription factor              binding affinities and a novel machine learning approach was used to build dynamic regulatory networks              that make full use of these time-series data. In parallel, to further prioritize the identified regulatory             genes we mapped super-enhancers with dynamic profiles during differentiation and associated them to              target genes using correlation between expression and epigenomic data. Among the key regulatory nodes              controlled by dynamic super-enhancers in both lineages, we identified aryl hydrocarbon receptor (AhR),              a known receptor for environmental toxins. Knock-down of AhR followed by RNA-seq was used to              identify AhR target genes in undifferentiated cells and in adipocytes and osteoblasts differentiated for 1 day.             We propose AhR to function as a novel regulator of mesenchymal multipotency.
We have generated time-series transcriptomic and epigenomic data             during the differentiation of bone marrow adipocytes and osteoblasts from             their shared mesenchymal precursor cells using RNA-seq and ChIP-seq for several histone modifications,              in order to identify the gene regulatory networks underlying these differentiation processes,              and to better understand their dynamics over time. RNA and chromatin were collected              at 6 different time points during both of the 15-day differentiation processes and active enhancers (H3K27ac),              promoters (H3K4me3) and transcribed regions (H3K36me3) were mapped in both lineages.              The identified time point-specific open chromatin regions were annotated for transcription factor              binding affinities and a novel machine learning approach was used to build dynamic regulatory networks              that make full use of these time-series data. In parallel, to further prioritize the identified regulatory             genes we mapped super-enhancers with dynamic profiles during differentiation and associated them to              target genes using correlation between expression and epigenomic data. Among the key regulatory nodes              controlled by dynamic super-enhancers in both lineages, we identified aryl hydrocarbon receptor (AhR),              a known receptor for environmental toxins. Knock-down of AhR followed by RNA-seq was used to              identify AhR target genes in undifferentiated cells and in adipocytes and osteoblasts differentiated for 1 day.             We propose AhR to function as a novel regulator of mesenchymal multipotency.