Back

Structural insight into the transcription activation mechanism of the phage Mor/C family activators

Shi, J.; Ye, Z.; Huang, Y.; Xu, L.; Xu, S.; Xie, L.; Chen, W.; Wang, L.; Feng, Z.; Song, Q.; Wang, S.; Feng, Y.; Lin, W.

2025-05-06 biochemistry
10.1101/2025.05.02.651988 bioRxiv
Show abstract

Bacteriophage Mu, a temperate phage that infects E. coli K-12 and other enteric bacteria, precisely controls its replication cycle through hijacking host RNA polymerase (RNAP) by the middle operon regulator Mor and the late gene transcription activator C. Though a dimeric arrangement and significant conformational changes are proposed for the distinct Mor/C family activators, the underlying transcription activation mechanism remains unclear. In this study, we present two cryo-EM structures of the transcription activation complex (Mor-TAC and C-TAC) with phage Mu middle and late gene promoters, respectively. Remarkably, the Mor/C activators bind to promoter DNA as a centrosymmetric tetramer rather than as the proposed dimer, concurrently stabilizing by the N-terminal dimerization domains and C-termini. The C-terminal DNA binding domains and two anti-{beta}-strands simultaneously interact with two adjacent DNA major grooves. The activators also engage a variety of interactions with the conserved domains (CTD, {sigma}70R4, and {beta} FTH) of RNAP, providing evidences for a recruitment mechanism. In addition, single-molecule FRET assays show that C significantly enhances RPitc formation, suggesting a different multi-step activation mechanism for C. Collectively, these findings reveal the unique transcription activation mechanism of tetrameric Mor/C family activators, unraveling a novel mode of phage hijacking and bacterial transcription regulation.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.