References of "Gerard, Déborah 50001838"
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See detailIdentification of genes under dynamic post-transcriptional regulation from time-series epigenomic data
Becker, Julia Christina UL; Gerard, Déborah UL; Ginolhac, Aurélien UL et al

in Epigenomics (2019)

Aim: Prediction of genes under dynamic post-transcriptional regulation from epigenomic data. Materials & methods: We used time-series profiles of chromatin immunoprecipitation-seq data of histone ... [more ▼]

Aim: Prediction of genes under dynamic post-transcriptional regulation from epigenomic data. Materials & methods: We used time-series profiles of chromatin immunoprecipitation-seq data of histone modifications from differentiation of mesenchymal progenitor cells toward adipocytes and osteoblasts to predict gene expression levels at five time points in both lineages and estimated the deviation of those predictions from the RNA-seq measured expression levels using linear regression. Results & conclusion: The genes with biggest changes in their estimated stability across the time series are enriched for noncoding RNAs and lineage-specific biological processes. Clustering mRNAs according to their stability dynamics allows identification of post-transcriptionally coregulated mRNAs and their shared regulators through sequence enrichment analysis. We identify miR-204 as an early induced adipogenic microRNA targeting Akr1c14 and Il1rl1. [less ▲]

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See detailTemporal enhancer profiling of parallel lineages identifies AHR and GLIS1 as regulators of mesenchymal multipotency
Gerard, Déborah UL; Schmidt, Florian; Ginolhac, Aurélien UL et al

in Nucleic Acids Research (2018)

Temporal data on gene expression and context-specific open chromatin states can improve identification of key transcription factors (TFs) and the gene regulatory networks (GRNs) controlling cellular ... [more ▼]

Temporal data on gene expression and context-specific open chromatin states can improve identification of key transcription factors (TFs) and the gene regulatory networks (GRNs) controlling cellular differentiation. However, their integration remains challenging. Here, we delineate a general approach for data-driven and unbiased identification of key TFs and dynamic GRNs, called EPIC-DREM. We generated time-series transcriptomic and epigenomic profiles during differentiation of mouse multipotent bone marrow stromal cell line (ST2) toward adipocytes and osteoblasts. Using our novel approach we constructed time-resolved GRNs for both lineages and identifed the shared TFs involved in both differentiation processes. To take an alternative approach to prioritize the identified shared regulators, we mapped dynamic super-enhancers in both lineages and associated them to target genes with correlated expression profiles. The combination of the two approaches identified aryl hydrocarbon receptor (AHR) and Glis family zinc finger 1 (GLIS1) as mesenchymal key TFs controlled by dynamic cell type-specific super-enhancers that become repressed in both lineages. AHR and GLIS1 control differentiation-induced genes and their overexpression can inhibit the lineage commitment of the multipotent bone marrow-derived ST2 cells. [less ▲]

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See detailAPPROACHES FOR IDENTIFICATION OF TRANSCRIPTIONAL AND POST-TRANSCRIPTIONAL REGULATORS OF MESENCHYMAL STEM CELL DIFFERENTIATION USING TIME-SERIES EPIGENOMIC DATA
Gerard, Déborah UL

Doctoral thesis (2017)

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 ... [more ▼]

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. [less ▲]

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