Yeast Crf1p is an activator with different roles in regulation of target genes.

Under stress conditions, ribosome biogenesis is downregulated. This process requires that expression of ribosomal RNA, ribosomal protein, and ribosome biogenesis genes be controlled in a coordinated fashion. The mechanistic Target of Rapamycin Complex 1 (mTORC1) participates in sensing unfavorable conditions to effect the requisite change in gene expression. In Saccharomyces cerevisiae, downregulation of ribosomal protein genes involves dissociation of the activator Ifh1p in a process that depends on Utp22p, a protein that also functions in pre-rRNA processing. Ifh1p has a paralog, Crf1p, which was implicated in communicating mTORC1 inhibition and hence was perceived as a repressor. We focus here on two ribosomal biogenesis genes, encoding Utp22p and the high mobility group protein Hmo1p, both of which are required for communication of mTORC1 inhibition to target genes. Crf1p functions as an activator on these genes as evidenced by reduced mRNA abundance and RNA polymerase II occupancy in a crf1Δ strain. Inhibition of mTORC1 has distinct effects on expression of HMO1 and UTP22; for example, on UTP22, but not on HMO1, the presence of Crf1p promotes the stable depletion of Ifh1p. Our data suggest that Crf1p functions as a weak activator, and that it may be required to prevent re-binding of Ifh1p to some gene promoters after mTORC1 inhibition in situations when Ifh1p is available. We propose that the inclusion of genes encoding proteins required for mTORC1-mediated downregulation of ribosomal protein genes in the same regulatory circuit as the ribosomal protein genes serves to optimize transcriptional responses during mTORC1 inhibition.

Targeting bacterial transcription factors for infection control: opportunities and challenges.

The rising threat of antibiotic resistance in pathogenic bacteria emphasizes the need for new therapeutic strategies. This review focuses on bacterial transcription factors (TFs), which play crucial roles in bacterial pathogenesis. We discuss the regulatory roles of these factors through examples, and we outline potential therapeutic strategies targeting bacterial TFs. Specifically, we discuss the use of small molecules to interfere with TF function and the development of transcription factor decoys, oligonucleotides that compete with promoters for TF binding. We also cover peptides that target the interaction between the bacterial TF and other factors, such as RNA polymerase, and the targeting of sigma factors. These strategies, while promising, come with challenges, from identifying targets to designing interventions, managing side effects, and accounting for changing bacterial resistance patterns. We also delve into how Artificial Intelligence contributes to these efforts and how it may be exploited in the future, and we touch on the roles of multidisciplinary collaboration and policy to advance this research domain.Abbreviations: AI, artificial intelligence; CNN, convolutional neural networks; DTI: drug-target interaction; HTH, helix-turn-helix; IHF, integration host factor; LTTRs, LysR-type transcriptional regulators; MarR, multiple antibiotic resistance regulator; MRSA, methicillin resistant Staphylococcus aureus; MSA: multiple sequence alignment; NAP, nucleoid-associated protein; PROTACs, proteolysis targeting chimeras; RNAP, RNA polymerase; TF, transcription factor; TFD, transcription factor decoying; TFTRs, TetR-family transcriptional regulators; wHTH, winged helix-turn-helix.