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A framework to identify epigenome and transcription factor crosstalk

Arnold, Phil. A framework to identify epigenome and transcription factor crosstalk. 2013, Doctoral Thesis, University of Basel, Faculty of Science.

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Official URL: http://edoc.unibas.ch/diss/DissB_10426

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Abstract

While changes in chromatin are integral to transcriptional reprogramming during cellular differentiation, it is currently unclear how chromatin modifications are targeted to specific loci. To systematically identify transcription factors (TFs) that can direct chromatin changes during cell fate decisions, we model the genome-wide dynamics of chromatin marks in terms of computationally predicted TF binding sites. By applying this computational approach to a time course of Polycomb-mediated H3K27me3 marks during neuronal differentiation of murine stem cells, we identify several motifs that likely regulate dynamics of this chromatin mark. Among these, the motifs bound by REST and by the SNAIL family of TFs are predicted to transiently recruit H3K27me3 in neuronal progenitors. We validate these predictions experimentally and show that absence of REST indeed causes loss of H3K27me3 at target promoters in trans, specifically at the neuronal progenitor state. Moreover, using targeted transgenic insertion, we show that promoter fragments containing REST or SNAIL binding sites are sufficient to recruit H3K27me3 in cis, while deletion of these sites results in loss of H3K27me3. These findings illustrate that the occurrence of TF binding sites can determine chromatin dynamics. Local determination of Polycomb activity by Rest and Snail motifs exemplifies such TF based regulation of chromatin. Furthermore, our results show that key TFs can be identified ab initio through computational modeling of epigenome datasets using a modeling approach that we make readily accessible.
Advisors:Nimwegen, Erik van
Committee Members:Schübeler, Dirk
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (van Nimwegen)
UniBasel Contributors:Arnold, Phil
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:10426
Thesis status:Complete
Number of Pages:158 S.
Language:English
Identification Number:
edoc DOI:
Last Modified:22 Jan 2018 15:51
Deposited On:23 Jul 2013 09:51

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