Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655 (ChIP-exo data set)

Transcriptional regulation enables cells to respond to environmental changes. Yet, among the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, only 185 have been experimentally characterized. Here we developed an integrated workflow that contains the prediction of TFs using machine learning and comprehensive experimental validation using a suite of genome-wide experiments. Applying this workflow we: 1) computationally identified 16 candidate genes encoding uncharacterized TFs; 2) confirmed that ten of these 16 are TF candidates that showed 255 DNA binding sites; 3) found high-confidence TF-binding sequence motifs for six of the ten TFs; 4) reconstructed the regulons of the ten TFs by determining gene expression change upon deletion of each TF; and 5) further determined the regulatory roles of three TFs (YdcI, YeiE and YiaJ) to be regulating acetate metabolism, iron homeostasis under iron limited condition, and utilization of L-ascorbate, respectively. Together, these results demonstrate how the integrated workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs. This workflow can be applied to less studied bacteria for systematic discovery and characterization of their transcriptional regulatory networks. Identification of genome-wide bindings for ten uncharacterized transcripton factors, including 20 replicates