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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-03-01
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| Series: | Frontiers in Genetics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00033/full |
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| author | Sebastian eZeidler Sebastian eZeidler Sebastian eZeidler Cornelia eMeckbach Rebecca eTacke Farah S. eRaad Farah S. eRaad Angelica eRoa Angelica eRoa Shizuka eUchida Shizuka eUchida Wolfram Hubertus eZimmermann Wolfram Hubertus eZimmermann Edgar eWingender Edgar eWingender Mehmet eGültas |
| author_facet | Sebastian eZeidler Sebastian eZeidler Sebastian eZeidler Cornelia eMeckbach Rebecca eTacke Farah S. eRaad Farah S. eRaad Angelica eRoa Angelica eRoa Shizuka eUchida Shizuka eUchida Wolfram Hubertus eZimmermann Wolfram Hubertus eZimmermann Edgar eWingender Edgar eWingender Mehmet eGültas |
| author_sort | Sebastian eZeidler |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T22:38:07Z |
| format | Article |
| id | doaj.art-d722dfa5d5f44d03bf5a94a656a8c3a2 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-11T22:38:07Z |
| publishDate | 2016-03-01 |
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| series | Frontiers in Genetics |
| spelling | doaj.art-d722dfa5d5f44d03bf5a94a656a8c3a22022-12-22T00:47:53ZengFrontiers Media S.A.Frontiers in Genetics1664-80212016-03-01710.3389/fgene.2016.00033175814Computational detection of stage-specific transcription factor clusters during heart developmentSebastian eZeidler0Sebastian eZeidler1Sebastian eZeidler2Cornelia eMeckbach3Rebecca eTacke4Farah S. eRaad5Farah S. eRaad6Angelica eRoa7Angelica eRoa8Shizuka eUchida9Shizuka eUchida10Wolfram Hubertus eZimmermann11Wolfram Hubertus eZimmermann12Edgar eWingender13Edgar eWingender14Mehmet eGültas15University Medical Center GöttingenUniversity Medical Center GöttingenDZHK (German Center for Cardiovascular Research), Partner site GöttingenUniversity Medical Center GöttingenUniversity Medical Center GöttingenUniversity Medical Center GöttingenDZHK (German Center for Cardiovascular Research), Partner site GöttingenUniversity Medical Center GöttingenDZHK (German Center for Cardiovascular Research), Partner site GöttingenGoethe University FrankfurtDZHK (German Center for Cardiovascular Research), Partner site Rhein-Main, Frankfurt am MainUniversity Medical Center GöttingenDZHK (German Center for Cardiovascular Research), Partner site GöttingenUniversity Medical Center GöttingenDZHK (German Center for Cardiovascular Research), Partner site GöttingenUniversity Medical Center GöttingenTranscription 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.http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00033/fullcardiomyogenesisMarkov clusteringMatrixCatchTF interactionEngineered heart muscle |
| spellingShingle | Sebastian eZeidler Sebastian eZeidler Sebastian eZeidler Cornelia eMeckbach Rebecca eTacke Farah S. eRaad Farah S. eRaad Angelica eRoa Angelica eRoa Shizuka eUchida Shizuka eUchida Wolfram Hubertus eZimmermann Wolfram Hubertus eZimmermann Edgar eWingender Edgar eWingender Mehmet eGültas Computational detection of stage-specific transcription factor clusters during heart development Frontiers in Genetics cardiomyogenesis Markov clustering MatrixCatch TF interaction Engineered heart muscle |
| 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 | cardiomyogenesis Markov clustering MatrixCatch TF interaction Engineered heart muscle |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2016.00033/full |
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