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

摘要: Transcription factors (TFs) regulate gene expression in living organisms. In higher organisms, TFs often interact non-random combinations with each other to control transcription. Understanding the interactions is key decipher mechanisms underlying tissue development. The aim of this study was analyze co-occurring transcription factor binding sites (TFBSs) a time series dataset from new cell-culture model human heart muscle development order identify common as well specific TFBS pairs promoter regions regulated genes which can be essential enhance cardiac developmental processes. To end, we separated available RNAseq into five temporally defined groups: (i) mesoderm induction stage; (ii) early specification (iii) late (iv) maturation (v) stage, where these stages characterized by unique differentially expressed (DEGs). for applied MatrixCatch algorithm, successful method deduce experimentally described promoters DEGs. Although DEGs stage are distinct, our results show that pair networks predicted all quite similar. Thus, extend utilizing Markov clustering algorithm (MCL) perform network analysis. Using extended approach, able separate several clusters highlight stage-specific co-occurences between TFBSs. Our approach has revealed either (NFAT or HMGIY clusters) (SMAD AP-1 individual stages. Several likely play an important role during cardiomyogenesis. Further, have shown related TFBSs indicate potential synergistic antagonistic switch different Additionally, suggest cardiomyogenesis follows hourglass already proven Arabidopsis and some vertebrates. This investigation helps us get better understanding how affected combination TFs. Such knowledge may help understand basic principles stem cell differentiation cardiomyocytes.