Project description:Here, we investigated the impact of Stx2 phage carriage on Escherichia coli (E. coli) K-12 MG1655 host gene expression. Using quantitative RNA-seq analysis, we compared the transcriptome of naïve MG1655 and the lysogens carrying the Stx2 phage of the 2011 E. coli O104:H4 outbreak strain or of the E. coli O157:H7 strain PA8, which share high degree of sequence similarity.
Project description: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.
Project description: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.
