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Excitation and adaptation in bacteria-a model signal transduction system that controls taxis and spatial pattern formation.

Othmer HG, Xin X, Xue C - Int J Mol Sci (2013)

Bottom Line: Here we discuss models which reproduce many of the important behaviors of the system.The important characteristics of the signal transduction system are excitation and adaptation, and the latter implies that the transduction system can function as a "derivative sensor" with respect to the ligand concentration in that the DC component of a signal is ultimately ignored if it is not too large.We also discuss some of the spatial patterns observed in populations and indicate how cell-level behavior can be embedded in population-level descriptions.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, University of Minnesota, Minneapolis,MN 55455, USA. othmer@math.umn.edu.

ABSTRACT
The machinery for transduction of chemotactic stimuli in the bacterium E. coli is one of the most completely characterized signal transduction systems, and because of its relative simplicity, quantitative analysis of this system is possible. Here we discuss models which reproduce many of the important behaviors of the system. The important characteristics of the signal transduction system are excitation and adaptation, and the latter implies that the transduction system can function as a "derivative sensor" with respect to the ligand concentration in that the DC component of a signal is ultimately ignored if it is not too large. This temporal sensing mechanism provides the bacterium with a memory of its passage through spatially- or temporally-varying signal fields, and adaptation is essential for successful chemotaxis. We also discuss some of the spatial patterns observed in populations and indicate how cell-level behavior can be embedded in population-level descriptions.

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Responses of receptor Tsr in vitro and cheRcheB mutants with varied expression levels of Tar or Tsr. (A) Simulated responses to MeAsp of cheRcheB mutant cells expressing only Tar at 1 (○), 2 (□) and 6 (◇) times the native level; (B) Simulated responses to serine of cheRcheB mutant cells expressing only Tsr at 0.3 (○), 0.7 (□) and 5 (◇) times the native level; (C) Simulated responses to MeAsp of cheRcheB mutant cells expressing Tsr at the native level and Tar at 0 (*), 0.6 (◇), 1 (○), 2 (□) and 6 (△) times the native level; (D) Simulated responses to serine of cheRcheB mutant cells expressing Tsr at the native level and Tar at 0 (*), 0.6 (◇), 1 (○), 2 (□) and 6 (△) times the native level; (E) Simulated responses to serine by the receptor Tsr at the methylation state QQQQ (○), QEQE (◇) and EEEE (△).
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f7-ijms-14-09205: Responses of receptor Tsr in vitro and cheRcheB mutants with varied expression levels of Tar or Tsr. (A) Simulated responses to MeAsp of cheRcheB mutant cells expressing only Tar at 1 (○), 2 (□) and 6 (◇) times the native level; (B) Simulated responses to serine of cheRcheB mutant cells expressing only Tsr at 0.3 (○), 0.7 (□) and 5 (◇) times the native level; (C) Simulated responses to MeAsp of cheRcheB mutant cells expressing Tsr at the native level and Tar at 0 (*), 0.6 (◇), 1 (○), 2 (□) and 6 (△) times the native level; (D) Simulated responses to serine of cheRcheB mutant cells expressing Tsr at the native level and Tar at 0 (*), 0.6 (◇), 1 (○), 2 (□) and 6 (△) times the native level; (E) Simulated responses to serine by the receptor Tsr at the methylation state QQQQ (○), QEQE (◇) and EEEE (△).

Mentions: We use this model to explain the observed ultrahigh cooperativity. We simulate the responses to methyl-aspartate (MeAsp) and serine in the cheRcheB mutants with different expression levels of the receptors Tar and Tsr and compare to Sourjik and Berg’s experiments. In Figure 7(A) are shown the responses to MeAsp in the cheRcheB mutants with only Tar expressed (compare to Figure 2(a) in [39]), in Figure 7(B) are the responses to serine with only Tsr expressed (compare to Figure 2(b) in [39]), in Figure 7(C) are the responses to MeAsp with native-level Tsr and varied-level Tar expressed (compare to Figure 1(c) in [39]), and in Figure 7(D) are shown the responses to serine with native-level Tar and varied-level Tsr expressed (compare to Figure 1(d) in [39]). We also simulate the kinase activity responses of Tsr to serine in vitro, shown in Figure 7(E) and compare to Li and Weis’ experiments (Figure 3 in [67]). Data fitting with the Hill function shows that the simulation results have quantitative agreement with the measures. The details of the simulations, such as parameter values and data fitting, are omitted here but documented in [145]. Our modeling on a single trimer of dimers and extension to a cluster of trimer of dimers indicates that the strongly-coupled trimer of dimers is the core unit for signaling function, that the short-range interaction between dimer members of a trimer, which we call the intratrimer interaction, plays a key role, and that the long-range interaction between trimers in a loosely coupled cluster, which we call the intertrimer interaction, is responsible for ultrahigh cooperativity.


Excitation and adaptation in bacteria-a model signal transduction system that controls taxis and spatial pattern formation.

Othmer HG, Xin X, Xue C - Int J Mol Sci (2013)

Responses of receptor Tsr in vitro and cheRcheB mutants with varied expression levels of Tar or Tsr. (A) Simulated responses to MeAsp of cheRcheB mutant cells expressing only Tar at 1 (○), 2 (□) and 6 (◇) times the native level; (B) Simulated responses to serine of cheRcheB mutant cells expressing only Tsr at 0.3 (○), 0.7 (□) and 5 (◇) times the native level; (C) Simulated responses to MeAsp of cheRcheB mutant cells expressing Tsr at the native level and Tar at 0 (*), 0.6 (◇), 1 (○), 2 (□) and 6 (△) times the native level; (D) Simulated responses to serine of cheRcheB mutant cells expressing Tsr at the native level and Tar at 0 (*), 0.6 (◇), 1 (○), 2 (□) and 6 (△) times the native level; (E) Simulated responses to serine by the receptor Tsr at the methylation state QQQQ (○), QEQE (◇) and EEEE (△).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3676780&req=5

f7-ijms-14-09205: Responses of receptor Tsr in vitro and cheRcheB mutants with varied expression levels of Tar or Tsr. (A) Simulated responses to MeAsp of cheRcheB mutant cells expressing only Tar at 1 (○), 2 (□) and 6 (◇) times the native level; (B) Simulated responses to serine of cheRcheB mutant cells expressing only Tsr at 0.3 (○), 0.7 (□) and 5 (◇) times the native level; (C) Simulated responses to MeAsp of cheRcheB mutant cells expressing Tsr at the native level and Tar at 0 (*), 0.6 (◇), 1 (○), 2 (□) and 6 (△) times the native level; (D) Simulated responses to serine of cheRcheB mutant cells expressing Tsr at the native level and Tar at 0 (*), 0.6 (◇), 1 (○), 2 (□) and 6 (△) times the native level; (E) Simulated responses to serine by the receptor Tsr at the methylation state QQQQ (○), QEQE (◇) and EEEE (△).
Mentions: We use this model to explain the observed ultrahigh cooperativity. We simulate the responses to methyl-aspartate (MeAsp) and serine in the cheRcheB mutants with different expression levels of the receptors Tar and Tsr and compare to Sourjik and Berg’s experiments. In Figure 7(A) are shown the responses to MeAsp in the cheRcheB mutants with only Tar expressed (compare to Figure 2(a) in [39]), in Figure 7(B) are the responses to serine with only Tsr expressed (compare to Figure 2(b) in [39]), in Figure 7(C) are the responses to MeAsp with native-level Tsr and varied-level Tar expressed (compare to Figure 1(c) in [39]), and in Figure 7(D) are shown the responses to serine with native-level Tar and varied-level Tsr expressed (compare to Figure 1(d) in [39]). We also simulate the kinase activity responses of Tsr to serine in vitro, shown in Figure 7(E) and compare to Li and Weis’ experiments (Figure 3 in [67]). Data fitting with the Hill function shows that the simulation results have quantitative agreement with the measures. The details of the simulations, such as parameter values and data fitting, are omitted here but documented in [145]. Our modeling on a single trimer of dimers and extension to a cluster of trimer of dimers indicates that the strongly-coupled trimer of dimers is the core unit for signaling function, that the short-range interaction between dimer members of a trimer, which we call the intratrimer interaction, plays a key role, and that the long-range interaction between trimers in a loosely coupled cluster, which we call the intertrimer interaction, is responsible for ultrahigh cooperativity.

Bottom Line: Here we discuss models which reproduce many of the important behaviors of the system.The important characteristics of the signal transduction system are excitation and adaptation, and the latter implies that the transduction system can function as a "derivative sensor" with respect to the ligand concentration in that the DC component of a signal is ultimately ignored if it is not too large.We also discuss some of the spatial patterns observed in populations and indicate how cell-level behavior can be embedded in population-level descriptions.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, University of Minnesota, Minneapolis,MN 55455, USA. othmer@math.umn.edu.

ABSTRACT
The machinery for transduction of chemotactic stimuli in the bacterium E. coli is one of the most completely characterized signal transduction systems, and because of its relative simplicity, quantitative analysis of this system is possible. Here we discuss models which reproduce many of the important behaviors of the system. The important characteristics of the signal transduction system are excitation and adaptation, and the latter implies that the transduction system can function as a "derivative sensor" with respect to the ligand concentration in that the DC component of a signal is ultimately ignored if it is not too large. This temporal sensing mechanism provides the bacterium with a memory of its passage through spatially- or temporally-varying signal fields, and adaptation is essential for successful chemotaxis. We also discuss some of the spatial patterns observed in populations and indicate how cell-level behavior can be embedded in population-level descriptions.

Show MeSH