Background:The loss of McpC has been shown to reduce chemotaxis to 19 of the 20 amino acids. Results: McpC can directly bind 11 amino acids and indirectly sense four others. Conclusion: McpC can sense a variety of amino acids by using two discrete mechanisms. Significance: We elucidate the mechanisms by which a single receptor can sense a wide variety of ligands.

Adaptation has a crucial role in the gradient-sensing mechanism that underlies bacterial chemotaxis. The Escherichia coli chemotaxis pathway uses a single adaptation system involving reversible receptor methylation. In Bacillus subtilis, the chemotaxis pathway seems to use three adaptation systems. One involves reversible receptor methylation, although quite differently than in E. coli. The other two involve CheC, CheD and CheV, which are chemotaxis proteins not found in E. coli. Remarkably, no one system is absolutely required for adaptation or is independently capable of generating adaptation. In this review, we discuss these three novel adaptation systems in B. subtilis and propose a model for their integration.

ABSTRACTBacteria possess multiple mechanisms to survive exposure to various chemical stresses and antimicrobial compounds. In the enteric bacteriumEscherichia coli, three homologous transcription factors—MarA, SoxS, and Rob—play a central role in coordinating this response. Three separate systems are known to regulate the expression and activities of MarA, SoxS, and Rob. However, a number of studies have shown that the three do not function in isolation but rather are coregulated through transcriptional cross talk. In this work, we systematically investigated the extent of transcriptional cross talk in themar-sox-robregulon. While the three transcription factors were found to have the potential to regulate each other's expression when ectopically expressed, the only significant interactions observed under physiological conditions were betweenmarandrobsystems. MarA, SoxS, and Rob all activate themarRABpromoter, more so when they are induced by their respective inducers: salicylate, paraquat, and decanoate. None of the three proteins affects thesoxSpromoter, though unexpectedly, it was mildly repressed by decanoate by an unknown mechanism. SoxS is the only one of the three proteins to repress therobpromoter. Surprisingly, salicylate somewhat activates transcription ofrob, while decanoate represses it a bit. Rob, in turn, activates not only its downstream promoters in response to salicylate but also themarRABpromoter. These results demonstrate that themarandrobsystems function together in response to salicylate.

During chemotaxis toward asparagine by Bacillus subtilis, the ligand is thought to bind to the chemoreceptor McpB on the exterior of the cell and induce a conformational change. This change affects the degree of phosphorylation of the CheA kinase bound to the cytoplasmic region of the receptor. Until recently, the sensing domains of the B. subtilis receptors were thought to be structurally similar to the well studied Escherichia coli fourhelical bundle. However, sequence analysis has shown the sensing domains of receptors from these two organisms to be vastly different. Homology modeling of the sensing domain of the B. subtilis asparagine receptor McpB revealed two tandem PAS domains. McpB mutants having alanine substitutions in key arginine and tyrosine residues of the upper PAS domain but not in any residues of the lower PAS domain exhibited a chemotactic defect in both swarm plates and capillary assays. Thus, binding does not appear to occur across any dimeric surface but within a monomer. A modified capillary assay designed to determine the concentration of attractant where chemotaxis is most sensitive showed that when Arg-111, Tyr-121, or Tyr-133 is mutated to an alanine, much more asparagine is required to obtain an active chemoreceptor. Isothermal titration calorimetry experiments on the purified sensing domain showed a K D to asparagine of 14 M, with the three mutations leading to less efficient binding. Taken together, these results reveal not only a novel chemoreceptor sensing domain architecture but also, possibly, a different mechanism for chemoreceptor activation.Many species of bacteria are able to sense their proximal chemical environment and move toward more favorable locations through a process known as chemotaxis. At the heart of all known bacterial chemotaxis pathways is a two-component signal transduction system involving the CheA histidine kinase and the CheY response regulator. In the Gram-positive bacterium Bacillus subtilis, the CheA kinase forms a stable complex with the chemotaxis receptors, also known as methyl-accepting chemotaxis proteins (MCPs), and the CheW and CheV adaptor proteins (1). When the receptors sense attractant molecules, they enhance the rate of CheA autophosphorylation, which in turn causes CheY-P concentrations to increase (2-4). In B. subtilis, the binding of CheY-P to the cytoplasmic face of the flagellum increases the likelihood of smooth runs (5). By biasing the duration and frequency of run and tumble swimming events, the bacterium is able to migrate up gradients of attractant molecules. In addition to this core signal transduction module, the B. subtilis pathway also possesses two CheY-P phosphatases and a number of regulatory proteins involved in sensory adaptation (6, 7). One of the remarkable features of the chemotaxis system is its ability to adapt the bacteria to their surrounding environment so that relative changes in the chemical concentrations can be sensitively detected.The chemotaxis receptors have previously been shown to be long, ␣-helical homodime...

Here we generate FRET biosensors for guanine exchange factors (GEFs) by inserting a fluorescent protein pair in a structural “hinge” common to many GEFs. Fluorescent biosensors can map the activation of signaling molecules in space and time, but it has not been possible to quantify how different activation events affect one another or contribute to a specific cell behavior. By imaging the GEF biosensors in the same cells as red-shifted biosensors of Rho GTPases, we can apply partial correlation analysis to parse out the extent to which each GEF contributes to the activation of a specific GTPase in regulating cell movement. Through analysis of spontaneous cell protrusion events we identify when and where the GEF Asef regulates the GTPases Cdc42 and Rac1 to control cell edge dynamics. This approach exemplifies a powerful means to elucidate the real-time connectivity of signal transduction networks.