Dynamics and Control of Flagella Assembly in Salmonella typhimurium

Abstract

The food-borne pathogen Salmonella typhimurium is a common cause of infections and diseases in a wide range of hosts. One of the major virulence factors associated to the infection process is flagella, which helps the bacterium swim to its preferred site of infection inside the host, the M-cells (Microfold cells) lining the lumen of the small intestine. The expression of flagellar genes is controlled by an intricate regulatory network. In this work, we investigate two aspects of flagella regulation and assembly: (a) distribution of the number of flagella in an isogenic population of bacteria and (b) dynamics of gene expression post cell division. More precisely, in a population of bacteria, we note a normal distribution of number of flagella assembled per cell. How is this distribution controlled, and what are the key regulators in the network which help the cell achieve this? In the second question, we explore the role of protein secretion in dictating gene expression dynamics post cell-division (when the number of hook basal bodies on the cell surface is reduced by a factor of two). We develop a mathematical model and perform stochastic simulations to address these questions. Simulations of the model predict that two accessory regulators of flagella gene expression, FliZ and FliT, have significant roles in maintaining population level distribution of flagella. In addition, FliT and FlgM were predicted to control the level and temporal order of flagellar gene expression when the cell adapts to post cell division consequences. Further, the model predicts that, the FliZ and FliT dependent feedback loops function under certain thresholds, alterations in which can substantially affect kinetics of flagellar genes. Thus, based on our results we propose that, the proteins FlgM, FliZ, and FliT, thought to have accessory roles in regulation of flagella, likely play a critical role controlling gene expression during cell division, and frequency distribution of flagella.

Keywords: Salmonella typhimurium; flagella; gene expression dynamics; gene regulation; mathematical model.

Figure 1

Regulatory network controlling expression of flagellar genes in S. typhimurium. The master regulator of flagella FlhD₄C₂ is at the top hierarchy of the regulatory cascade. Once it is expressed upon receiving multiple internal and external signals, a series of regulatory events take place for timely and proper expression of flagellar genes. The initial events have been represented by green arrows. These include activation of class 2 genes encoding HBB (hook, basal body) by FlhD₄C₂. At the same time, FlhD₄C₂ also activates the genes encoding five regulators—FliA (σ factor), FlgM (anti- σ factor), FliD (cap protein), FliT (anti- FlhD₄C₂ factor), and FliZ (regulator). The subsequent events have been depicted by brown arrows which include post-translational regulation through formation of two protein complexes FliA-FlgM (complex of FliA and FlgM) and FliT-FliD (complex of FliD and FliT). These complexes get disassociated only after complete assembly of HBB. The subsequent events after disassociation of these two protein complexes have been shown by purple dotted arrows. As the protein complexes get disrupted, FlgM and FliD are exported out of the cell (represented by φ), leaving free FliA and FliT inside the cell. Consequently, FliA carries out the next set of events represented by blue arrows. FliA activates class 3 genes encoding filament, motor and chemotaxis proteins. At the same time, FliA also activates itself and other class 2 genes encoding FliZ, FlgM, FliD and FliT. Finally, two feedback loops, represented by red edges, act on the master regulator FlhD₄C₂. These include FliZ dependent positive and FliT dependent negative feedback loops. FliZ acts as a repressor of a non-flagellar protein YdiV, which participates in nutritional status (of the cell) dependent regulation of FlhD₄C₂. FliT forms protein complex with FlhD₄C₂ and prevents FlhD₄C₂-dependent activation of class two genes.

Figure 2

Dynamics of flagellar class 2 and class 3 gene expression in (A) wild type, (B) ΔflgM, (C) ΔfliZ, (D) ΔfliT, and (E) ΔfliD. Class 2 and class 3 genes have been represented by solid and dashed lines, respectively. Here, class 2 genes represent the genes encoding the constituent proteins of HBB. Class 3 genes include the genes encoding filament, motor and chemotaxis proteins. A. U. denotes arbitrary units. For mutants, the expression levels of class 2 and class 3 genes have been normalized with respect to those in wild type and subsequently plotted. The plot for wild type shows the characteristic delay in class 3 gene expression. FlgM is responsible for this delay as class 2 and class 3 genes are expressed at similar time in ΔflgM as shown in B. Deletion of the activators FliZ and FliD results in reduced expression levels of class 2 and class 3 genes, while, deletion of the repressor FliT leads to increase in the expression levels. Standard deviation in each curve is <10% of the mean values.

Figure 3

Distribution of flagella number (HBB number) in an isogenic population of (A) wild type, (B) ΔfliZ, (C) ΔfliT mutant cells at different starvation levels (represented by α). The HBB number follows a normal distribution in a wild type population. FliZ and FliT control HBB numbers in a nutritional status dependent manner. The influence of nutritional level on FliZ's regulatory effect is more than that of FliT.

Figure 4

Dynamics of flagellar class 2 and class 3 gene expressions in (A) wild type, (B) ΔflgM, and (C) ΔfliT post cell division. Class 2 and class 3 genes have been represented by solid and dashed lines, respectively. A. U. denotes arbitrary units. For mutants, the expression levels of class 2 and class 3 genes have been normalized with respect to those in wild type and subsequently plotted. The delay in class 3 gene expression is also seen post cell division, similar to the observed phenomena during transition from non-flagellated to flagellated state. ΔflgM mutant shows altered hierarchy of class 2 and class 3 genes in a daughter cell, whereas, ΔfliT mutant leads to higher expression levels similar to its effect in a mother cell. Standard deviation in each curve is <10% of the mean values.

Figure 5

Effects of alterations in strength of FliZ and FliT feedback loops on class 2 gene expression. Here, class 2 gene expression represents the mean flagella number of a population of 10,000 cells. The values have been normalized to that of a wild type population. Reduction in the strengths of the feedback loops has a stronger influence on population level flagella number. Flagella number (and kinetics of flagellar gene expression) is sensitive to the strengths of the feedback loops.

Figure 6

Class 2 and class 3 gene expression with varying FlgM secretion rate in (A) wild type, (B) ΔfliZ, and (C) ΔfliT. Class 2 and class 3 gene expression with varying FliD secretion rate in (D) wild type and (E) ΔfliZ. Class 2 and class 3 genes have been represented by Black and Gray solid lines, respectively. A. U. denotes arbitrary units.

Figure 7

Effect of starvation on class 3/class2 gene ratio in (A) wild type, (B) ΔfliZ, and (C) ΔflgM. For mutants, the expression levels of class 2 and class 3 genes have been normalized with respect to those in wild type and subsequently plotted. In wild type, the ratio remains unaffected to changes in starvation level (α). ΔfliZ and ΔflgM mutants show altered pattern of this ratio. FliZ's role in maintaining the ratio is dependent on starvation level.