A novel transcription factor PunR and Nac are involved in purine and purine nucleoside transporter punC regulation in E. coli

Abstract

Many genes in bacterial genomes are of unknown function, often referred to as y-genes. Recently, novel analytic methods have divided bacterial transcriptomes into independently modulated sets of genes (iModulons). Functionally annotated iModulons that contain y-genes lead to testable hypotheses to elucidate y-gene function. Inversely correlated expression of a putative transporter gene, ydhC, relative to purine biosynthetic genes, has led to the hypothesis that it encodes a purine-related transporter and revealed a LysR-family regulator, YdhB, with a predicted 23-bp palindromic binding motif. RNA-Seq analysis of a ydhB knockout mutant confirmed the YdhB-dependent activation of ydhC in the presence of adenosine. The deletion of either the ydhC or the ydhB gene led to a substantially decreased growth rate for E. coli in minimal medium with adenosine as the nitrogen source, as well as with inosine or guanosine. Taken together, we provide clear evidence that YdhB activates the expression of the ydhC gene that encodes a novel purine transporter in E. coli. We propose that the genes ydhB and ydhC be re-named as punR and punC, respectively.

Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

“…The dashboard is user-friendly and researchers can search or browse the details of iModulons, TRN, genes, or regulators of interest. Such an examination gives 1) a guide to the study of molecular level mechanisms 17,18 or 2) systems level mechanisms, such as those of resource allocation through changes in the transcriptome composition between conditions 7,11 .…”

Machine Learning Uncovers a Data-Driven Transcriptional Regulatory Network for the Crenarchaeal Thermoacidophile Sulfolobus acidocaldarius

“…Furthermore, the condition-dependent activity levels of each iModulon correspond to the activity of the underlying genetic signals and regulators. Despite the difference in approach (i.e., data analytics vs. direct molecular methods), iModulons recapitulate many known regulons from the literature, and have accurately predicted new genetic targets for regulators ( Sastry et al, 2019 ; Poudel et al, 2020 ; Rychel et al, 2020 ) and elucidated gene functions ( Rodionova et al, 2020 , 2021 ). This top-down approach allows for an unbiased method for reconstructing the TRN of prokaryotic organisms of interest, including archaea.…”

Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius

“…Despite the difference in approach (i.e. data analytics vs direct molecular methods), iModulons recapitulate many known regulons from the literature, and have accurately predicted new genetic targets for regulators (Sastry et al, 2019;Poudel et al, 2020;Rychel et al, 2020) and elucidated gene functions (Rodionova et al, 2020(Rodionova et al, , 2021. This top-down approach allows for an unbiased method for reconstructing the TRN of prokaryotic organisms of interest, including archaea.…”