The nitrogen phosphotransferase regulator PtsN (EIIANtr) regulates inorganic polyphosphate production in Escherichia coli
Bowlin, M. Q.; Long, A. R.; Huffines, J. T.; Gray, M. J.
Show abstract
Inorganic polyphosphate (polyP) is synthesized by bacteria under stressful environmental conditions and acts by a variety of mechanisms to promote cell survival. While the kinase that synthesizes polyP (PPK, enocoded by the ppk gene) is well known, little is understood about how environmental stress signals lead to activation of this enzyme. Previous work has shown that the transcriptional regulators DksA, RpoN ({sigma}54), and RpoE ({sigma}24) positively regulate polyP production, but not ppk transcription, in Escherichia coli. In this work, we set out to examine the role of the alternative sigma factor RpoN and nitrogen starvation stress response pathways in controlling polyP synthesis in more detail. In the course of these experiments, we identified GlnG, GlrR, PhoP, PhoQ, RapZ, and GlmS as proteins that affect polyP production, and uncovered a central role for the nitrogen phosphotransferase regulator PtsN (EIIANtr) in a polyP regulatory pathway, acting upstream of DksA, downstream of RpoN, and apparently independently of RpoE. However, none of these regulators appears to act directly on PPK, and the mechanism(s) by which they modulate polyP production remain unclear. Unexpectedly, we also found that the pathways that regulate polyP production vary depending not only on the stress condition applied, but also on the composition of the media in which the cells were grown before exposure to polyP-inducing stress. These results constitute substantial progress towards deciphering the regulatory networks driving polyP production under stress, but highlight the remarkable complexity of this regulation and its connections to a broad range of stress-sensing pathways. IMPORTANCEBacteria respond to changes in their environments with a complex regulatory network that controls the expression and activity of a wide range of effectors important for their survival. This stress response network is critical for the virulence of pathogenic bacteria and for the ability of all bacteria to grow in natural environments. Inorganic polyphosphate (polyP) is an evolutionarily ancient and almost universally conserved stress response effector that plays multiple roles in virulence, stress response, and survival in diverse organisms. This work provides new insights into the connections between well characterized nitrogen starvation and cell envelope stress response signaling pathways and the production of polyP in Escherichia coli.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Pyrimidine sufficiency is required for Sae two-component system signaling in Staphylococcus aureus 97%
- Sugar-phosphate metabolism regulates stationary phase entry and stalk elongation in Caulobacter crescentus 97%
- Phosphorylation on PstP regulates cell wall metabolism and antibiotic tolerance in Mycobacterium smegmatis 96%
Similar papers in this journal
Similar papers in this journal
- Genetic evidence for functional diversification of gram-negative intermembrane phospholipid transporters 97%
- Crosstalk between guanosine nucleotides regulates cellular heterogeneity in protein synthesis during nutrient limitation 97%
- Phosphate starvation decouples cell differentiation from DNA replication control in the dimorphic bacterium Caulobacter crescentus 96%
Similar papers in this journal
- YhcB (DUF1043), a novel cell division protein conserved across gamma-proteobacteria 96%
- Prochlorococcus phage ferredoxin: structural characterization and electron transfer to cyanobacterial sulfite reductases 95%
- Cyclic AMP competitively inhibits periplasmic phosphatases to coordinate nutritional growth with competence development of Haemophilus influenzae 95%
"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.