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Systematic identification of transcriptional regulatory modules from protein–protein interaction networks

Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF b...

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Autores principales: Diez, Diego, Hutchins, Andrew Paul, Miranda-Saavedra, Diego
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874207/
https://www.ncbi.nlm.nih.gov/pubmed/24137002
http://dx.doi.org/10.1093/nar/gkt913
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author Diez, Diego
Hutchins, Andrew Paul
Miranda-Saavedra, Diego
author_facet Diez, Diego
Hutchins, Andrew Paul
Miranda-Saavedra, Diego
author_sort Diez, Diego
collection PubMed
description Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF binding, cell type-specific gene expression and protein–protein interactions. rTRM was applied to reconstruct the TRMs specific for embryonic stem cells (ESC) and hematopoietic stem cells (HSC), neural progenitor cells, trophoblast stem cells and distinct types of terminally differentiated CD4(+) T cells. The ESC and HSC TRM predictions were highly precise, yielding 77 and 96 proteins, of which ∼75% have been independently shown to be involved in the regulation of these cell types. Furthermore, rTRM successfully identified a large number of bridging proteins with known roles in ESCs and HSCs, which could not have been identified using genomic approaches alone, as they lack the ability to bind specific DNA sequences. This highlights the advantage of rTRM over other methods that ignore PPI information, as proteins need to interact with other proteins to form complexes and perform specific functions. The prediction and experimental validation of the co-factors that endow master regulatory TFs with the capacity to select specific genomic sites, modulate the local epigenetic profile and integrate multiple signals will provide important mechanistic insights not only into how such TFs operate, but also into abnormal transcriptional states leading to disease.
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spelling pubmed-38742072013-12-28 Systematic identification of transcriptional regulatory modules from protein–protein interaction networks Diez, Diego Hutchins, Andrew Paul Miranda-Saavedra, Diego Nucleic Acids Res Methods Online Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF binding, cell type-specific gene expression and protein–protein interactions. rTRM was applied to reconstruct the TRMs specific for embryonic stem cells (ESC) and hematopoietic stem cells (HSC), neural progenitor cells, trophoblast stem cells and distinct types of terminally differentiated CD4(+) T cells. The ESC and HSC TRM predictions were highly precise, yielding 77 and 96 proteins, of which ∼75% have been independently shown to be involved in the regulation of these cell types. Furthermore, rTRM successfully identified a large number of bridging proteins with known roles in ESCs and HSCs, which could not have been identified using genomic approaches alone, as they lack the ability to bind specific DNA sequences. This highlights the advantage of rTRM over other methods that ignore PPI information, as proteins need to interact with other proteins to form complexes and perform specific functions. The prediction and experimental validation of the co-factors that endow master regulatory TFs with the capacity to select specific genomic sites, modulate the local epigenetic profile and integrate multiple signals will provide important mechanistic insights not only into how such TFs operate, but also into abnormal transcriptional states leading to disease. Oxford University Press 2014-01-01 2013-10-16 /pmc/articles/PMC3874207/ /pubmed/24137002 http://dx.doi.org/10.1093/nar/gkt913 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods Online
Diez, Diego
Hutchins, Andrew Paul
Miranda-Saavedra, Diego
Systematic identification of transcriptional regulatory modules from protein–protein interaction networks
title Systematic identification of transcriptional regulatory modules from protein–protein interaction networks
title_full Systematic identification of transcriptional regulatory modules from protein–protein interaction networks
title_fullStr Systematic identification of transcriptional regulatory modules from protein–protein interaction networks
title_full_unstemmed Systematic identification of transcriptional regulatory modules from protein–protein interaction networks
title_short Systematic identification of transcriptional regulatory modules from protein–protein interaction networks
title_sort systematic identification of transcriptional regulatory modules from protein–protein interaction networks
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874207/
https://www.ncbi.nlm.nih.gov/pubmed/24137002
http://dx.doi.org/10.1093/nar/gkt913
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