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김동혁

Kim, Donghyuk
Systems Biology and Machine Learning Lab.
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Systems Evaluation of Less-Characterized Transcription Factors

Author(s)
Kim, Donghyuk
Issued Date
2019-11-23
URI
https://scholarworks.unist.ac.kr/handle/201301/78767
Citation
New Horizons in Biotechnology International Conference
Abstract
Transcriptional regulation enables cells to respond to environmental changes. Of the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, 185 have been experimentally identified, but ChIP methods have been used to fully characterize only a few dozen. Identifying these remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to identify 16 candidate TFs from over a hundred uncharacterized genes. As a result, we captured a total of 255 DNA binding peaks for ten candidate TFs resulting in six high confidence binding motifs, reconstructing the regulons of these ten TFs by determining gene expression changes upon deletion of each TF. Among newly evaluated TFs, yieP was further investigated for its role in 3-hydroxypropionate (3-HP) resistance in E. coli. Integration of ChIP-exo and transcriptome analysis revealed a number of target genes for YieP transcriptional regulation. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of less-characterized TFs in parallel.
Publisher
New Horizons in Biotechnology

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