Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development

Abstract

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.

Trichloroethylene perturbs HNF4a expression and activity in the developing chick heart

“…Its cardiac expression is consistent with the idea that it may be uniquely involved in the organogenesis stage of this organ. Consistent with the data shown here, a stage-specific examination of transcription factor clusters during cardiomyocyte differentiation by embryonic stem cells in vitro identified a temporally restricted co-expression of HNF4a with AP1 during a stage corresponding to early cardiomyocyte differentiation (Zeidler et al, 2016).…”

Computational identification of tissue-specific transcription factor cooperation in ten cattle tissues

“…The homeodomain factor NKX 2-5 is a well-studied TF which plays essential roles in healthy heart development and disease [58, 59]. Being a member of high-mobility group A ( HMGA ) non-histone chromosomal proteins, HMGA 1 is an important TF during heart development and growth as well as cardiomyocytic cell growth regulation [8, 60, 61].…”

Identifying Cattle Breed-Specific Partner Choice of Transcription Factors during the African Trypanosomiasis Disease Progression Using Bioinformatics Analysis

“…In order to gain a better understanding of the underlying molecular mechanism of AAT in different tissues and compare the results of both breeds, we created cooperation networks for each tissue based on its specific TF pairs as suggested in our previous studies [26,47,66,67]. The nodes represent the TFs and the edges represent their co-operation in these networks which are presented in Figure 3.…”