title: Adaptable functionality of transcriptional feedback in bacterial two-component systems.
abstract: A widespread mechanism of bacterial signaling occurs through two-component systems, comprised of a sensor histidine kinase (SHK) and a transcriptional response regulator (RR). The SHK activates RR by phosphorylation. The most common two-component system structure involves expression from a single operon, the transcription of which is activated by its own phosphorylated RR. The role of this feedback is poorly understood, but it has been associated with an overshooting kinetic response and with fast recovery of previous interrupted signaling events in different systems. Mathematical models show that overshoot is only attainable with negative feedback that also improves response time. Our models also predict that fast recovery of previous interrupted signaling depends on high accumulation of SHK and RR, which is more likely in a positive feedback regime. We use Monte Carlo sampling of the parameter space to explore the range of attainable model behaviors. The model predicts that the effective feedback sign can change from negative to positive depending on the signal level. Variations in two-component system architectures and parameters may therefore have evolved to optimize responses in different bacterial lifestyles. We propose a conceptual model where low signal conditions result in a responsive system with effectively negative feedback while high signal conditions with positive feedback favor persistence of system output.
authors.affiliation: Department of Bioengineering, Rice University, Houston, Texas, USA.
authors.forename: J Christian J
authors.initials: JC
authors.lastname: Ray
authors.forename: Oleg A
authors.initials: OA
authors.lastname: Igoshin
mesh_terms: D000465:Algorithms; D018407:Bacterial Physiological Phenomena / Q000235:genetics*; D001426:Bacterial Proteins / Q000235:genetics / Q000502:physiology*; D019295:Computational Biology; D025461:Feedback, Physiological / Q000502:physiology*; D008954:Models, Biological; D009010:Monte Carlo Method; D015398:Signal Transduction; D014158:Transcription, Genetic / Q000502:physiology*; D020798:Two-Hybrid System Techniques
chemical_list: D001426:Bacterial Proteins