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Identification of candidate master transcription factors within enhancer-centric transcriptional regulatory networks

View ORCID ProfileAlexander J. Federation, Donald R. Polaski, Christopher J. Ott, Angela Fan, Charles Y. Lin, James E. Bradner
doi: https://doi.org/10.1101/345413
Alexander J. Federation
1Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts 02215
2Altius Institute for Biomedical Sciences, Seattle, WA 98121
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Donald R. Polaski
1Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts 02215
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Christopher J. Ott
1Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts 02215
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Angela Fan
1Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts 02215
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Charles Y. Lin
1Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts 02215
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James E. Bradner
1Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts 02215
3Novartis Institute for Biomedical Sciences, Cambridge, MA 02139
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Abstract

Regulation of gene expression through binding of transcription factors (TFs) to cis-regulatory elements is highly complex in mammalian cells. Genome-wide measurement technologies provide new means to understand this regulation, and models of TF regulatory networks have been built with the goal of identifying critical factors. Here, we report a network model of transcriptional regulation between TFs constructed by integrating genomewide identification of active enhancers and regions of focal DNA accessibility. Network topology is confirmed by published TF ChIP-seq data. By considering multiple methods of TF prioritization following network construction, we identify master TFs in well-studied cell types, and these networks provide better prioritization than networks only considering promoter-proximal accessibility peaks. Comparisons between networks from similar cell types show stable connectivity of most TFs, while master regulator TFs show dramatic changes in connectivity and centrality. Applying this method to study chronic lymphocytic leukemia, we prioritized several network TFs amenable to pharmacological perturbation and show that compounds targeting these TFs show comparable efficacy in CLL cell lines to FDA-approved therapies. The construction of transcriptional regulatory network (TRN) models can predict the interactions between individual TFs and predict critical TFs for development or disease.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 12, 2018.
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Identification of candidate master transcription factors within enhancer-centric transcriptional regulatory networks
Alexander J. Federation, Donald R. Polaski, Christopher J. Ott, Angela Fan, Charles Y. Lin, James E. Bradner
bioRxiv 345413; doi: https://doi.org/10.1101/345413
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Identification of candidate master transcription factors within enhancer-centric transcriptional regulatory networks
Alexander J. Federation, Donald R. Polaski, Christopher J. Ott, Angela Fan, Charles Y. Lin, James E. Bradner
bioRxiv 345413; doi: https://doi.org/10.1101/345413

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