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Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development
Transcription factors (TFs) regulate gene expression in living organisms. In higher organisms, TFs often interact in non-random combinations with each other to control gene transcription. Understanding the interactions is key to decipher mechanisms underlying tissue development. The aim of this stud...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4804722/ https://www.ncbi.nlm.nih.gov/pubmed/27047536 http://dx.doi.org/10.3389/fgene.2016.00033 |
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author | Zeidler, Sebastian Meckbach, Cornelia Tacke, Rebecca Raad, Farah S. Roa, Angelica Uchida, Shizuka Zimmermann, Wolfram-Hubertus Wingender, Edgar Gültas, Mehmet |
author_facet | Zeidler, Sebastian Meckbach, Cornelia Tacke, Rebecca Raad, Farah S. Roa, Angelica Uchida, Shizuka Zimmermann, Wolfram-Hubertus Wingender, Edgar Gültas, Mehmet |
author_sort | Zeidler, Sebastian |
collection | PubMed |
description | Transcription factors (TFs) regulate gene expression in living organisms. In higher organisms, TFs often interact in non-random combinations with each other to control gene transcription. Understanding the interactions is key to decipher mechanisms underlying tissue development. The aim of this study was to analyze co-occurring transcription factor binding sites (TFBSs) in a time series dataset from a new cell-culture model of human heart muscle development in order to identify common as well as specific co-occurring TFBS pairs in the promoter regions of regulated genes which can be essential to enhance cardiac tissue developmental processes. To this end, we separated available RNAseq dataset into five temporally defined groups: (i) mesoderm induction stage; (ii) early cardiac specification stage; (iii) late cardiac specification stage; (iv) early cardiac maturation stage; (v) late cardiac maturation stage, where each of these stages is characterized by unique differentially expressed genes (DEGs). To identify TFBS pairs for each stage, we applied the MatrixCatch algorithm, which is a successful method to deduce experimentally described TFBS pairs in the promoters of the DEGs. Although DEGs in each stage are distinct, our results show that the TFBS pair networks predicted by MatrixCatch for all stages are quite similar. Thus, we extend the results of MatrixCatch utilizing a Markov clustering algorithm (MCL) to perform network analysis. Using our extended approach, we are able to separate the TFBS pair networks in several clusters to highlight stage-specific co-occurences between TFBSs. Our approach has revealed clusters that are either common (NFAT or HMGIY clusters) or specific (SMAD or AP-1 clusters) for the individual stages. Several of these clusters are likely to play an important role during the cardiomyogenesis. Further, we have shown that the related TFs of TFBSs in the clusters indicate potential synergistic or antagonistic interactions to switch between different stages. Additionally, our results suggest that cardiomyogenesis follows the hourglass model which was already proven for Arabidopsis and some vertebrates. This investigation helps us to get a better understanding of how each stage of cardiomyogenesis is affected by different combination of TFs. Such knowledge may help to understand basic principles of stem cell differentiation into cardiomyocytes. |
format | Online Article Text |
id | pubmed-4804722 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-48047222016-04-04 Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development Zeidler, Sebastian Meckbach, Cornelia Tacke, Rebecca Raad, Farah S. Roa, Angelica Uchida, Shizuka Zimmermann, Wolfram-Hubertus Wingender, Edgar Gültas, Mehmet Front Genet Genetics Transcription factors (TFs) regulate gene expression in living organisms. In higher organisms, TFs often interact in non-random combinations with each other to control gene transcription. Understanding the interactions is key to decipher mechanisms underlying tissue development. The aim of this study was to analyze co-occurring transcription factor binding sites (TFBSs) in a time series dataset from a new cell-culture model of human heart muscle development in order to identify common as well as specific co-occurring TFBS pairs in the promoter regions of regulated genes which can be essential to enhance cardiac tissue developmental processes. To this end, we separated available RNAseq dataset into five temporally defined groups: (i) mesoderm induction stage; (ii) early cardiac specification stage; (iii) late cardiac specification stage; (iv) early cardiac maturation stage; (v) late cardiac maturation stage, where each of these stages is characterized by unique differentially expressed genes (DEGs). To identify TFBS pairs for each stage, we applied the MatrixCatch algorithm, which is a successful method to deduce experimentally described TFBS pairs in the promoters of the DEGs. Although DEGs in each stage are distinct, our results show that the TFBS pair networks predicted by MatrixCatch for all stages are quite similar. Thus, we extend the results of MatrixCatch utilizing a Markov clustering algorithm (MCL) to perform network analysis. Using our extended approach, we are able to separate the TFBS pair networks in several clusters to highlight stage-specific co-occurences between TFBSs. Our approach has revealed clusters that are either common (NFAT or HMGIY clusters) or specific (SMAD or AP-1 clusters) for the individual stages. Several of these clusters are likely to play an important role during the cardiomyogenesis. Further, we have shown that the related TFs of TFBSs in the clusters indicate potential synergistic or antagonistic interactions to switch between different stages. Additionally, our results suggest that cardiomyogenesis follows the hourglass model which was already proven for Arabidopsis and some vertebrates. This investigation helps us to get a better understanding of how each stage of cardiomyogenesis is affected by different combination of TFs. Such knowledge may help to understand basic principles of stem cell differentiation into cardiomyocytes. Frontiers Media S.A. 2016-03-23 /pmc/articles/PMC4804722/ /pubmed/27047536 http://dx.doi.org/10.3389/fgene.2016.00033 Text en Copyright © 2016 Zeidler, Meckbach, Tacke, Raad, Roa, Uchida, Zimmermann, Wingender and Gültas. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Zeidler, Sebastian Meckbach, Cornelia Tacke, Rebecca Raad, Farah S. Roa, Angelica Uchida, Shizuka Zimmermann, Wolfram-Hubertus Wingender, Edgar Gültas, Mehmet Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development |
title | Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development |
title_full | Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development |
title_fullStr | Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development |
title_full_unstemmed | Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development |
title_short | Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development |
title_sort | computational detection of stage-specific transcription factor clusters during heart development |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4804722/ https://www.ncbi.nlm.nih.gov/pubmed/27047536 http://dx.doi.org/10.3389/fgene.2016.00033 |
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