Transcriptional and post-transcriptional mechanisms modulate cyclopropane fatty acid synthase through small RNAs in Escherichia coli
Bianco, C.; Caballero-Rothar, N. N.; Ma, X.; Farley, K.; Vanderpool, C. K.
Show abstract
The small RNA (sRNA) RydC strongly activates cfa, which encodes the cyclopropane fatty acid synthase. Previous work demonstrated that RydC activation of cfa increases conversion of unsaturated fatty acids to cyclopropanated fatty acids in membrane lipids and changes the biophysical properties of membranes, making cells more resistant to acid stress. The conditions and regulators that control RydC synthesis had not previously been identified. In this study, we demonstrate that RydC regulation of cfa is important for resistance to membrane-disrupting conditions. We identify a GntR-family transcription factor, YieP, that represses rydC transcription and show that YieP indirectly regulates cfa through RydC. YieP positively autoregulates its own transcription. We further identify additional sRNA regulatory inputs that contribute to control of RydC and cfa. The translation of yieP is repressed by the Fnr-dependent sRNA, FnrS, making FnrS an indirect activator of rydC and cfa. Conversely, RydC activity on cfa is antagonized by the OmpR-dependent sRNA OmrB. Altogether, this work illuminates a complex regulatory network involving transcriptional and post-transcriptional inputs that link control of membrane biophysical properties to multiple environmental signals. ImportanceBacteria experience many environmental stresses that challenge their membrane integrity. To withstand these challenges, bacteria sense what stress is occurring and mount a response that protects membranes. Previous work documented the important roles of small RNA (sRNA) regulators in membrane stress responses. One sRNA, RydC, helps cells cope with membrane-disrupting stresses by promoting changes in the types of lipids incorporated into membranes. In this study, we identified a regulator, YieP, that controls when RydC is produced, and additional sRNA regulators that modulate YieP levels and RydC activity. These findings illuminate a complex regulatory network that helps bacteria sense and respond to membrane stress.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Defining the networks that connect RNase III and RNase J-mediated regulation of primary and specialized metabolism in Streptomyces venezuelae 97%
- Effects of multiple cell regulators on curli gene expression in Escherichia coli 96%
- The impact of leadered and leaderless gene structures on translation efficiency, transcript stability, and predicted transcription rates in Mycobacterium smegmatis 96%
Similar papers in this journal
- Disruptions in Outer Membrane-Peptidoglycan Interactions Enhance Bile Salt Resistance in O-antigen-Producing E. coli 95%
- Loss of Bacterial Cell Pole Stabilization in Caulobacter crescentus Sensitizes to Outer Membrane Stress and Peptidoglycan-Directed Antibiotics 95%
- Contributions of Ccr4 and Gcn2 to the translational response of C. neoformans to host-relevant stressors and Integrated Stress Response induction 95%
Similar papers in this journal
- The fitness landscape of the African Salmonella Typhimurium ST313 strain D23580 reveals unique properties of the pBT1 plasmid 96%
- Discovery and characterization of a Gram-positive Pel polysaccharide biosynthetic gene cluster 96%
- Klebsiella pneumoniae type VI secretion system-mediated microbial competition is PhoPQ controlled and reactive oxygen species dependent 96%
Similar papers in this journal
- Genome-wide analysis of in vivo CcpA binding with and without its key co-factor HPr in the major human pathogen group A Streptococcus. 94%
- Specialized and shared functions of diguanylate cyclases and phosphodiesterases in Streptomyces development. 94%
- Species-specific secretion of ESX-5 type VII substrates is determined by the linker 2 of EccC5 94%
"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.