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Liebal et al., (2012). Proteolysis of beta-galactosidase following SigmaB activation in Bacillus subtilis

October 2018, model of the month by Thawfeek M Varusai
Original model: BIOMD0000000460


Bacteria adapt to environmental challenges by altering their gene expression program. An effective method to achieve this is through regulating the activity of transcription factors. In Bacillus subtilis, several extracellular factors such as acids, heat and oxidative stress activate an important transcription factor called sigma B (σB). The molecular mechanism of σB-dependent general stress response is well studied [1,2]. A process known as ‘partner switch’ ensures the activation of σB under stress. The authors of this paper [3] study the dynamic response of σB activity and propose a mechanistic explanation for it.


Partner switch process: This mechanism facilitates the activation of σB under stress conditions in B. subtillus. This process involves the sigma factor B (σB), an anti-sigma factor (RsbW) and an anti-anti-sigma factor (RsbV). Under stress conditions, RsbV binds and sequesters RsbW, which is a σB inhibitor. This activates σB to adapt to the stress. When the conditions are normal, RsbV is phosphorylated and will not be able to sequester RsbW effectively, thereby resulting in restricted σB activity.
Although, a lot is known about the ‘partner switch’ process of σB regulation, some mechanisms are still unclear. For instance, the mechanisms restricting σB activity in B. subtillus and accomplishing the transient nature of the σB response is still largely unknown.

Figure 1

Figure 1. Comparison of the models showing their process diagrams, their mathematical representation and their ability to fit optimally to the given experiments. Only the ‘post-transcriptional instability’ model can reproduce the observations sufficiently. Therefore, we assume this model forms the basis of an explanation for the data. Figures adapted from [3].

Mathematical Modelling

To identify the underlying mechanism of σB activity regulation, the authors build three competing dynamic models and test experimental data against the three hypotheses (figure 1):
1. Transcription Inhibition
2. σB Proteolysis Model
3. Post-transcriptional Instability Model
Ordinary differential equations (ODE) were used to build the three models. Parameters were estimated based on dose response and time course experiments of β-galactosidase activity (output of the transfected lacz gene in the lac operon). The models are small with three ODEs and about nine parameters. Zero-order and first-order reaction kinetics were used to represent protein synthesis and degradation. Hill equation kinetics were used to capture protein transcription processes. Systems Biology Toolbox 2 in Matlab was used to build and analyse the models.


The authors found a surprisingly similar response of σB activity in wild type B. subtillus and a mutant B. subtillus with RsbW knockdown. Hene they postulated three possible hypotheses to explain the observed empirical behaviour (figure 1). The authors built and analysed different models to test theses hypotheses. The best fitting hypothesis was the post-transcriptional instability model, which suggested that the instability of either mRNA or protein of β-galactosidase as potential causes. Subsequent experiments confirm proteolytic decay as the cause for the decrease in β-galactosidase signal. Thus, the authors identified a σB-induced β-galactosidase instability in the B. subtillus system. Furthermore, predictions from models were experimentally validated. This imparts a high predictive power to the models used in this paper.


The authors postulate three possible hypotheses to explain the observed transient profile of β-galactosidase activity. Performing experiments to test which of these hypothesis holds true would have proven to be a laborious and expensive process. In contrast, building models to computationally predict the appropriate hypothesis is effective and powerful. Subsequently, these predictions can be empirically confirmed. Thus, the use of dynamic modelling in this work is quite resourceful. The activation of σB in B. subtillus is a well studied mechanism. On the contrary, there is little information on its de-activation mechanism. This work sheds light on the dynamics of σB activity shutdown in B. subtillus and the use of the β-galactosidase system to study this mechanism. The authors show how the lac operon may not be ideal to study σB de-activation since it is prone to degradation. Further research is required to characterize the de-activation of σB factors in in B. subtillus.


  1. M. Hecker, J. Pane-Farre and U. Vo¨ lker. (2007), SigB-Dependent General Stress Response in Bacillus subtilis and Related Gram-Positive Bacteria, Annu. Rev. Microbiol., 2007, 61, 215–236.
  2. W. G.Haldenwang. (1995), The sigma factors of Bacillus subtilis, Microbiol. Rev., 1995, 59(1), 1–30.
  3. Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, Völker U, Wolkenhauer O. (2012), Proteolysis of beta-galactosidase following SigmaB activation in Bacillus subtilis, Mol Biosyst. 2012;8:1806–1814.