SEARCH

SEARCH BY CITATION

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgements
  8. References
  9. Supporting Information

The clustering of membrane-bound receptors plays an essential role in various biological systems. A notable model system for studying this phenomenon is the bacterial chemosensory cluster that allows motile bacteria to navigate along chemical gradients in their environment. While the basic structure of these chemosensory clusters is becoming clear, their dynamic nature and operation are not yet understood. By measuring the fluorescence polarization of tagged receptor clusters in live Escherichia coli cells, we provide evidence for stimulus-induced dynamics in these sensory clusters. We find that when a stimulus is applied, the packing of the receptors slowly decreases and that the process reverses when the stimulus is removed. Consistent with these physical changes we find that the effective cooperativity of the kinase response slowly evolves in the presence of a stimulus. Time-lapse fluorescence imaging indicates that, despite these changes, the receptor clusters do not generally dissociate upon ligand binding. These data reveal stimulus-dependent plasticity in chemoreceptor clusters.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgements
  8. References
  9. Supporting Information

Bacterial chemoreceptors are elongated transmembrane proteins that sense chemical changes in the local environment of the bacterium as it swims through a medium and bias its motion towards favourable directions (Berg, 2004; Hazelbauer et al., 2008). These receptors form large clusters containing thousands of molecules, first discovered in Escherichia coli (Maddock and Shapiro, 1993) and later found in many other bacterial species (Briegel et al., 2009). The E. coli chemosensory system has became a tractable model system for exploring the basic properties of sensory signalling in receptor clusters.

The conformation of a typical chemoreceptor in E. coli, such as Tar or Tsr, is controlled, on the one hand, by the binding of ligands to the periplasmic domain of the receptor, which favours the inactive conformation, and, on the other hand, by the methylation of specific sites at the cytoplasmic domain of the receptor, which favours the active conformation and thus opposes the effect of ligands. The methylation level of the receptors is regulated by the methylesterase CheB and the methyltransferase CheR and allows adaptation to prolonged stimuli. A dedicated cytoplasmic histidine kinase, CheA, is bound to the receptors, such that the autophosphorylation rate of the kinase is directly regulated by the conformational state of the receptors. The kinase, in turn, together with the dedicated cytoplasmic phosphatase CheZ, controls the phosphorylation level of the ‘response-regulator’ protein, CheY. This phosphoryl-transfer system belongs to a large family of sensory signalling pathways known as ‘two-component’ systems that control many adaptive responses in bacteria. In the chemotaxis system, phosphorylated CheY binds to the flagellar motors and regulates their rotational bias, and thus the swimming behaviour of the bacterium.

The discovery of chemosensory clusters has led to the suggestion that clustering promotes conformational coupling between the receptors (Bray et al., 1998; Duke and Bray, 1999). Coupling between signalling molecules in such arrays has been confirmed by demonstrating high cooperativity in the response of the kinase activity to external stimuli (Li and Weis, 2000; Gestwicki and Kiessling, 2002; Sourjik and Berg, 2004; Lai et al., 2005) and a non-linear relationship between the physical responses of poorly clustered receptors (Vaknin and Berg, 2007) or small functional units in nanodiscs (Amin and Hazelbauer, 2010) and that of the kinase activity in clusters. Ising-type models have been used to describe the signalling properties of such clusters (Mello and Tu, 2003; Shimizu et al., 2003), and subsequent analysis, based on the Monod–Wyman–Changeux (MWC) model, was able to explain many of the experimental observations (Sourjik and Berg, 2004; Mello and Tu, 2005; Keymer et al., 2006). However, these models could not explain the lack of adaptational coupling between the receptors (Sanders and Koshland, 1988; Lan et al., 2011). Thus, recent models have proposed alternative modes of coupling between the receptors via kinase interactions (Goldman et al., 2009), local adaptation (Lan et al., 2011), and dynamic coupling between receptors (Hansen et al., 2010).

Cumulative evidence indicates that clusters are large arrays of receptor trimers of dimers (Kim et al., 1999; Shimizu et al., 2000; Zhang et al., 2007; Hazelbauer et al., 2008; Briegel et al., 2012; Liu et al., 2012). This structure is promoted by the binding of CheA homodimers and a linker protein, CheW, to connect the cytoplasmic tips of receptors trimers. While the basic hexagonal order of these receptor arrays is becoming clear, the long-range arrangement and order in these arrays, as well as their dynamical properties and their response to stimuli, are still not understood. The effects of various stimuli on chemosensory clusters have been studied previously using various methods, including fluorescence imaging (Liberman et al., 2004; Vaknin and Berg, 2004), immunolabelling (Borrok et al., 2008; Wu et al., 2011), receptor cross-linking (Homma et al., 2004; Studdert and Parkinson, 2005), and fluorescence recovery after photo bleaching (Schulmeister et al., 2011); however, the emerging picture is still ambiguous. Recently, the effect of a stimulus on the conformation of chemoreceptor trimers of dimers in cells lacking CheA and CheW was detected in live cells by measuring the fluorescence polarization of chemoreceptors tagged with yellow fluorescent protein (YFP) (Vaknin and Berg, 2006; 2007). The measured polarization in these experiments was sensitive to the physical state of the receptors because of the Förster's (fluorescence) resonance energy transfer between the YFPs on different receptor dimers (homo-FRET). In this study, we used this type of fluorescence-polarization measurement, together with spectral-shift FRET measurements between CheY-mCherry and CheZ-YFP (Sourjik et al., 2007) and time-lapse fluorescence imaging, to study the effect of a stimulus on the physical and functional properties of clustered chemoreceptors in cells that express native levels of CheA and CheW.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgements
  8. References
  9. Supporting Information

The response of receptor clusters to stimuli was studied using three complementary methods. First, physical changes in YFP-tagged receptor clusters were probed directly by measuring the fluorescence polarization, which is sensitive to homo-FRET between the YFPs (Vaknin and Berg, 2006; 2007). Second, the global clustering pattern of the tagged receptors was studied using time-lapse fluorescence imaging. Third, the responses of the output kinase activity were measured by detecting the spectral-shift FRET between CheY-mCherry and CheZ-YFP (Sourjik et al., 2007). Experiments were performed using the E. coli VH1 strain (a derivative of the RP437 strain) that lacks all five chemoreceptors as well as the cheR, cheB, cheY and cheZ genes. Note that the VH1 strain is missing the genes encoding the adaptation enzymes (cheR and cheB) but retains the genes encoding the kinase (cheA) and the linker protein (cheW) that promote clustering. Working with cells that lack the adaptation enzymes was essential in order to allow for unambiguous detection of additional dynamical processes. To avoid potential complications concerning the metabolism of the ligand, we expressed the Tar chemoreceptor as the sole chemoreceptor in these cells and used its non-metabolized cognate ligand α-methylaspartate. To allow for the maximal effect of the ligand, which generally favours the inactive conformation of the receptors, we used a receptor mutant (TarQQQQ) that mimics a methylated receptor, which, in the absence of ligand, is biased towards the active conformation.

To measure the responses of the receptors to the stimulus in real time, the cells were placed in a gold-plated flow chamber similar to that developed by Berg and Block (Berg & Block, 1984). The kinetics of the flow through the chambers was measured using an intrinsically fluorescent tryptone broth (TB) medium, which consists mainly of amino acids, and measuring the rise time of the fluorescence. The half-maximum time was approximately 6 s, and 20 s after switching the flow to TB, the fluorescence level was well within saturation with accuracy better than 0.4% (Fig. S1).

A stimulus induces physical modulation in chemoreceptor clusters

Physical changes in receptor clusters were probed by measuring the fluorescence polarization of cell populations expressing YFP-tagged Tar as the sole chemoreceptor. Receptor expression was close to the native level and yielded distinct receptor clusters that could be observed directly by fluorescence imaging (Fig. 1A). Such clustering is expected to enhance the homo-FRET between trimers and thus to reduce the polarization. Indeed, as shown in Fig. 1A, the baseline fluorescence anisotropy measured for clustered receptors in cells that express CheA and CheW (red symbols) was significantly lower than that for poorly clustered receptors in cells that lack CheA and CheW (blue symbols). By tagging the same Tar receptors with the cyan fluorescent protein (CFP) instead of the YFP and measuring the spectral-shift FRET between CheY-mCherry and CheZ-YFP, we could directly assess the ability of the tagged receptors to modulate the output kinase activity (Sourjik et al., 2007). The CFP tag is used in these measurements as a structural element instead of the YFP to prevent interference with the CheZ-YFP. As seen in Fig. 1B, the tagged receptors could modulate the kinase activity in a dose-dependent manner that is similar to that of the untagged receptors.

figure

Figure 1. The response of clustered receptors.

A. Red symbols − an anisotropy trace measured for the YFP-tagged Tar receptors expressed as the sole receptor in VH1 cells. α-Methylaspartate (300 μM), a cognate ligand of Tar, was added and then removed at the times indicated by the arrows. Fluorescence images of representative cells are shown in the inset. Blue symbols − a similar trace measured for cells that lack CheA/CheW (UU1581 cells). Note that the higher level of fluorescence anisotropy in this case corresponds to reduced homo-FRET.

B. Normalized dose–responses of kinase activity to α-methylaspartate measured using spectral-shift FRET. Results are for VH1 cells that express either CFP-tagged (filled red symbols) or untagged (open black symbols) Tar as the sole chemoreceptor. Each stimulus was applied for approximately 50 s.

Download figure to PowerPoint

A typical anisotropy response of the YFP-tagged Tar receptors to their cognate ligand α-methylaspartate is shown in Fig. 1A (red symbols). Traces were corrected for a baseline drift (see Fig. S2). For comparison, an anisotropy response measured with poorly clustered receptors in cells lacking CheA and CheW is also shown in Fig. 1A (blue symbols). The anisotropy response in the absence of CheA and CheW was previously shown to reflect changes in homo-FRET as a result of conformational changes in the trimers (Vaknin and Berg, 2007).

Two distinct features separate the traces measured for clustered receptors in the presence of CheA/W from those measured in the absence of CheA/W. First, as noted above, the baseline anisotropy is significantly lower for the clustered receptors (higher homo-FRET), and second, a prominent, slowly varying component appears in the response of the clustered receptors. Interestingly, the slow part of the response appears to have logarithmic dynamics for a few hundred seconds after ligand addition or removal (Fig. 2A). To check the possibility that the observed dynamics might be related to ligand dynamics, we repeated these experiments with various concentrations of the ligand above the level that saturates the receptors (see Fig. 1B). The observed anisotropy dynamics were independent of the ligand concentration as long as the concentration was above the saturation level of approximately 100 μM (Fig. 2A), suggesting that ligand dynamics are not dominant. In addition, to check if the effect triggered by α-methylaspartate is specific to Tar receptors, we repeated the experiments with cells that express the Tsr chemoreceptor as the sole receptor. No effect of α-methylaspartate was observed with the Tsr receptors. However, a clear response was observed when serine, the cognate ligand of Tsr, was used as a ligand (Fig. S3). Thus, these slow changes in anisotropy most likely reflect changes in receptor packing that occur upon ligand binding.

figure

Figure 2. The dynamics of the anisotropy responses. The anisotropy response measured in VH1 cells that express the YFP-tagged Tar receptors.

A. The response to the addition (filled symbols) or removal (open symbols) of α-methylaspartate. The data are normalized to the maximal response. Different colours correspond to different concentrations of α-methylaspartate from 100 μM to 3000 μM. Note the logarithmic scale on the abscissa.

B. The response to the addition of 30, 40 or 100 μM α-methylaspartate. Note the linear scale on the abscissa.

Download figure to PowerPoint

When subsaturation stimuli were applied, the initial changes in anisotropy were smaller and decreased with lower ligand concentrations, but the initial response was still followed by a slower increase in anisotropy (Fig. 2B). Due to limited time resolution, the initial rapid response cannot be clearly separated from the following slower response, and thus we could not determine the dose–response behaviour of the instantaneous response of the receptors under these conditions. Slow changes in anisotropy were also observed with the Tar receptors in their native modification state, TarQEQE (Fig. S4).

Time-lapse fluorescence imaging

Given the observed tendency of the receptors to change their packing upon ligand binding, we examined the effect of the ligand on the overall receptor clustering. Using time-lapse imaging, we followed cells expressing tagged receptors in a flow-chamber while switching between normal buffer and α-methylaspartate-containing buffer (for details, see Experimental procedures). In the vast majority of the cells, the addition of the ligand had no detectable effect on the distribution of receptors within the cell (Fig. 3A; see also supplementary movies).

figure

Figure 3. The effect of a stimulus on receptor clustering.

A. Typical fluorescence images of VH1 cells expressing the YFP-tagged Tar receptors before (upper image) and after (lower image) the addition of α-methylaspartate (1 mM).

B. The change in the ‘contrast’ parameter (see Experimental procedures) in a population of 82 cells upon the addition of α-methylaspartate is shown as a function of the intrinsic localization in the cell, quantified as described in Experimental procedures; see also Frank et al. (2011).

C. Histogram of the normalized change in the contrast parameter in the population of cells shown in (B).

Download figure to PowerPoint

To quantify the effect of the ligand, we defined a ‘contrast’ parameter, C = (Imax/Imean) − 1, where Imax and Imean are the maximal and the mean intensities in the cell area respectively. The normalized change in this parameter upon ligand addition, ΔC/Cbuffer, is shown in Fig. 3B for a total of 82 cells and is plotted against the intrinsic receptor localization in the cell prior to ligand addition (see Experimental procedures for definition of the ‘localization’ parameter). As seen in Fig. 3B and C, in only a few cells (approximately 4% of the population) did we observe changes in clustering that appeared to be correlated with addition of ligand. As shown in Fig. 3B, the level of receptor localization was lower in these cells, indicating that the intrinsic clustering efficiency in these cells was lower even prior to the addition of the ligand. Overall, these data indicate that receptor clusters do not generally dissociate upon ligand binding.

A stimulus alters the transduction properties of receptor clusters

It is generally thought that chemoreceptor clustering leads to cooperativity in the responses of the kinase activity to a stimulus (Sourjik and Armitage, 2010). Therefore, the observed physical modulation of clusters (Figs 1A and 2) can be expected to affect the transduction properties of the cluster and, in particular, its cooperativity. Thus, we further assessed the effect of cluster modulation on the output kinase activity. Since the YFP tag is not required for the measurements described in this section, we opted to use the untagged Tar receptors in these experiments. We expressed the untagged Tar receptors in the same VH1 strain and measured the dynamics of the kinase activity responses to α-methylaspartate by monitoring the spectral-shift FRET between CheY-mCherry and CheZ-YFP, which were also expressed in these cells. In principle, physical modulation of the receptor cluster can affect the activity of the phosphatase CheZ, which is normally bound to the cluster. To avoid this potential complication and ensure that the FRET measurement reflects only changes in kinase activation, we used a mutant CheZ (CheZF98S) that does not bind to the cluster but retains phosphatase activity (Cantwell et al., 2003).

When a subsaturating concentration of α-methylaspartate was added, the kinase activity initially dropped quickly and then continued to decrease gradually (Fig. 4A; filled symbols). Brief exposure to a saturating ligand concentration did not diminish the subsequent dynamics (Fig. 4A; open symbols), indicating that the preceding equilibrium conditions are more important than the momentary changes in the ligand concentration. The slow part of the response was not affected by changes in the flow-rate of the medium between 400 and 2000 μl min−1 (Fig. 4B; red symbols). These kinase dynamics, seen with subsaturating stimuli, are qualitatively consistent with the dynamics of the receptors seen in Fig. 2B. In contrast, when a saturating stimulus was applied, the slow part of the kinase activity response was less prominent (Fig. 4B; black symbols). However, the lack of slow dynamics following a saturating stimulus might simply be due to the fact that the kinase activity is fully inhibited under these conditions and thus insensitive to changes in the clusters.

figure

Figure 4. The effect of a stimulus on kinase activity. The kinase activity in VH1 cells expressing the untagged Tar receptors was measured by the spectral-shift FRET between CheY-mCherry and CheZF98S-YFP (see Experimental procedures). The change in the kinase activity (relative to the prestimulated level) is shown for various sequences of addition and removal of α-methylaspartate.

A. Traces are shown for two experiments using a similar protocol in which 65 μM was maintained for the time marked by the grey bar. In the experiment represented by the open symbols, 100 μM was added transiently for a few seconds, and then the concentration was set back to 65 μM.

B. Responses to the addition of subsaturating (65 μM; red symbols) or saturating (100–300 μM; black symbols) concentrations. Flow rates ranged from 400 to 2000 μl min−1. Note the logarithmic scale on the abscissa.

C. Stimuli were added according to the protocols depicted by the bars: white represents buffer, light grey represents 65 μM, and dark grey represents 300 μM. The dashed line is a guide to the eye.

Download figure to PowerPoint

To check this possibility, we adopted the following protocol, using α-methylaspartate as the ligand. First, after equilibration without ligand, the cells were exposed to an intermediate ligand concentration of 65 μM for approximately 30 s while the kinase activity is monitored. Then, the cells were equilibrated with a saturating concentration of 300 μM for 300 s, and the kinase activity response to 65 μM was measured again. A typical kinase activity trace measured using this protocol is shown in Fig. 4C (upper plot). Clearly, the kinase activity at 65 μM depended on the preceding equilibrium conditions (0 or 300 μM). Thus, although no change in the kinase activity was observed during the exposure to 300 μM α-methylaspartate, the system clearly has changed during this time.

To explore this dynamic process further, we used a modified protocol. As before, the cells were first equilibrated in the absence of ligand, and the response to 65 μM α-methylaspartate was measured. Then, the cells were exposed to 300 μM α-methylaspartate for varying periods of time, after which the response to 65 μM α-methylaspartate was measured. As shown in Fig. 4C (lower plot), a brief exposure to an elevated ligand concentration had no effect on the kinase activity level when the ligand concentration was reduced back to 65 μM; however, as the exposure time was extended, the kinase activity level at 65 μM monotonically decreased (indicated by the dashed line in the figure). These data show that the signal-transduction properties during the exposure to saturating concentrations of ligand are dynamic. Note that the timescale over which these changes occur is comparable with that of the physical modulation shown in Figs 1 and 2.

The behaviour shown in Fig. 4 is consistent with the notion that the physical modulation of clusters affects the cooperativity of the kinase responses. To assess this conclusion more directly, we adopted the protocol depicted in Fig. 5A. We first measured the dose-dependence of the response of the kinase activity to increasing amounts of ligand while maintaining a ligand concentration of zero before and between stimuli, Fig. 5A (black line). The dose–response plot obtained in these measurements is shown in Fig. 5B (filled black symbols). We then repeated the experiment with the same cells after exposing them to 300 μM α-methylaspartate for 300 s using the protocol depicted in Fig. 5A (red line). In these experiments, the dose response was measured by applying varying concentrations of α-methylaspartate (10 μM to 300 μM) while maintaining the concentration at 300 μM between stimuli. The dose–response plot obtained in these measurements is shown in Fig. 5B (filled red symbols). The black and red lines in Fig. 5B correspond to Hill coefficients of 4.5 and 2.8 respectively. To verify that the response was independent of the protocol itself, we repeated these experiments several times using shorter protocol, similar to those used in Fig. 4C (upper plot), and obtained similar results, presented in Fig. 5B as open symbols.

figure

Figure 5. Stimulus-induced changes in cluster cooperativity. The dose–response of the kinase activity to α-methylaspartate measured in VH1 cells that express untagged Tar as the sole chemoreceptor.

A. Schematic description of the two protocols used in these experiments; first, cells were exposed to buffer before and between stimuli (black line); and second, cells were exposed to 300 μM for 300 s before the first stimulus and then between stimuli, which were applied from the highest to lowest concentration (red line). Each stimulus was applied for 50 s.

B. The dose response plots measured using the protocols described in (A). Filled black and red symbols correspond to the first and second protocols respectively. The open symbols correspond to a modified shorter protocol similar to the one used in Fig. 4C (upper plot).

Download figure to PowerPoint

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgements
  8. References
  9. Supporting Information

The data presented in this study demonstrate the dynamic nature of chemosensory arrays. We find that ligand binding loosens the overall packing of the receptors (Figs 1 and 2) and correspondingly decreases the effective cooperativity of the kinase responses (Figs 4 and 5). The emerging picture is summarized in Fig. 6. Time-lapse fluorescence imaging demonstrated that these changes do not, in most cases, lead to dissociation of the receptor arrays (Fig. 3).

figure

Figure 6. Summary of ligand-induced cluster dynamics. In the presence of ligand, the packing of the chemoreceptors in the arrays becomes less tight (lower plot; see Fig. 2) while, at the same time (blue arrows), the effective dose–response function for the kinase activity is modified (upper plot). The red and black dose–response curves correspond to the two packing states shown in the lower plot: before (black) and after (red) the ligand-induced changes in receptor arrays occurred (see Fig. 5).

Download figure to PowerPoint

Ligand binding changes the packing in receptor arrays

The observation that addition of α-methylaspartate affects the Tar clusters but not the Tsr clusters (Fig. S3) indicates that the effect of α-methylaspartate on the Tar receptors is most likely caused by its known binding interaction with the Tar receptor. Indeed, the concentration of α-methylaspartate that triggers the modulation of the receptor clusters (Figs 1 and 2) was well within the dynamic range of the normal kinase responses and, as previously documented (Mesibov and Adler, 1972), was approximately 10-fold higher than the corresponding aspartate concentration, presumably because of the lower affinity of α-methylaspartate to the Tar receptor. Thus, the slow modulation of Tar clusters is most likely triggered by the primary conformational response of the Tar receptor to ligand binding.

Several lines of evidence indicate that the dynamics of ligand binding do not play a dominant role in the observed anisotropy response. First, and most importantly, if the dynamics of ligand binding were important, the observed changes in fluorescence anisotropy would strongly depend on the concentration of the added ligand, with faster dynamics expected as the ligand concentration increased. However, the observed anisotropy response was insensitive to an increase in the concentration of the ligand to more than 10-fold higher than the saturation value (Fig. 2). Second, despite the fact that the ability of the cell to metabolize and internalize α-methylaspartate is very different than that for aspartate (Kay, 1971; Mesibov and Adler, 1972), both ligands triggered very similar dynamics (Fig. S5). Finally, the rate of transport of α-methylaspartate into the cytoplasm is expected to be very different from the rate of its secretion out of the cytoplasm. Thus, if the binding dynamics of the ligand were dominant, strongly asymmetrical response would be expected upon the addition or removal of the ligand. However, the dynamics were generally symmetrical (Fig. 2). We therefore conclude that the dynamics of the anisotropy responses following a stimulus represent the physical response of the receptor clusters to ligand binding.

Because of limitations in the stimulation time (Fig. S1), the data shown in Figs 1 and 2B do not allow us to separate the initial anisotropy change, which could potentially reflect the primary conformational response of the receptors to ligand binding (Vaknin and Berg, 2007; Sferdean et al., 2012), from the subsequent, more gradual increase in anisotropy. The gradual component of the response most likely reflects more complex molecular processes within the clusters. The non-exponential dynamics observed in Fig. 2 are consistent with such complex molecular dynamics. Given that the baseline anisotropy measured for receptors clustered with CheA/W is significantly lower (homo-FRET higher) than that measured in the absence of CheA/W (Fig. 1), it is expected that changes in receptor packing would affect the measured anisotropy by modulating the efficiency of inter-trimer, and possibly also intra-trimer, homo-FRET. To account for the increase in anisotropy following a stimulus, the cluster modulation ought to involve an overall decrease in the packing efficiency of the receptors. This scenario is generally consistent with the effect of the receptors modification state on their packing efficiency in vesicles (Besschetnova et al., 2008).

Receptor trimers of dimers tend to form extended receptor arrays supported by rings of CheA and CheW proteins that bind to the cytoplasmic tips of receptor trimers and induce a basic hexagonal order (Briegel et al., 2012; Liu et al., 2012). Upon ligand binding, a conformational change is propagating along the receptor to induce a conformational change in the receptor trimer tip, which ultimately induces a conformational change in the associated kinase. It is therefore conceivable that stimulus-induced conformational changes in the trimer tip, CheA and CheW junction might also have a subtle effect on the stability of the CheA/W rings, and thereby on receptor packing. Such an effect might be related, for example, to changes in the flexibility of the receptor trimer/CheA/CheW junctions (Kim et al., 2002; Swain et al., 2009; Parkinson, 2010). This scenario is consistent with the enhanced thermal stability of the receptors upon clustering (Frank et al., 2011) and with the observation that the dynamics of CheA association with the array can be altered by stimuli (Schulmeister et al., 2011).

The basic lattice constant of the receptor hexagonal array appears to be insensitive to stimuli (Khursigara et al., 2008; Briegel et al., 2011). Thus, it seems more likely that changes in the trimer junctions affect more global features of the array. In principle, changes in the trimer junctions can affect the long-range order of the receptor arrays and thus the overall packing of the receptors. Structural variations in receptor arrays might include molecular variations within each cluster, which can either be random or reflect changes between the core of the cluster and its edges. In particular, in addition to the primary CheA/W-mediated clustering, which fits the hexagonal order, direct associations between receptors have also been reported (Kentner et al., 2006). To the extent that such associations also occur in the presence of CheA/W, they have the potential to cause local distortions in the receptor array. Also, as mentioned above, fluorescence recovery after photobleaching (FRAP) experiments suggest that the association of the scaffold protein CheA with the array is dynamic and depends on ligand binding (Schulmeister et al., 2011). Such dynamics would imply a constant introduction of ‘defects’ to the array structure that could lead to local distortion of the array. Recent cryo-EM tomography studies have reported varying levels of disorder in receptor arrays that correlated with their signal-transduction properties (Khursigara et al., 2011). In addition, variability in the size of the arrays or in the stoichiometry of different components within the arrays might be expected both between different clusters within each cell or between cells. High-resolution fluorescence-imaging studies suggest that a continuous distribution of cluster sizes exist within each cell, an observation that is consistent with a dynamic model for cluster assembly (Greenfield et al., 2009).

Consistent with previous studies (Homma et al., 2004; Liberman et al., 2004; Vaknin and Berg, 2004; Studdert and Parkinson, 2005; Erbse and Falke, 2009; Briegel et al., 2011; Khursigara et al., 2011), we find that the overall integrity of the clusters is generally preserved in the presence of a stimulus (Fig. 3). Changes in the global clustering pattern was found in only a small fraction of the cells, which exhibited low-contrast localization prior to stimulation (Fig. 3B), possibly reflecting unfavourable conditions for clustering in these cells. The changes observed in the immunolabelling patterns of chemoreceptors upon stimulus (Borrok et al., 2008) might reflect changes in either receptor packing or overall clustering. More complex behaviour was observed in B. subtilis cells (Wu et al., 2011).

Ligand binding induces changes in signal transduction

In addition to ligand-induced modifications in the packing of receptor arrays, we find evidence for corresponding ligand-induced evolution in the intrinsic signal-transduction properties of these arrays. In general, we find that receptor-stimulated kinase activity depends not only on the current ligand concentration but also on the preceding equilibrium ligand concentration (Fig. 4). Moreover, the effect of prior exposure to a ligand is clearly dynamic and becomes more dominant as the time of exposure increases (Fig. 4A and C, lower plot). The timescale of this modulation of kinase activity is comparable to that of the physical modulation of clusters. These stimulus-induced changes in kinase activity can be viewed as a slow modulation in the effective cooperativity of the receptor-mediated response (Fig. 5). Such a correlation between the cooperativity of the kinase responses and the packing efficiency of the receptors is predicted by an understanding that the kinase cooperativity is, at least in large part, caused by receptor packing within clusters (Vaknin and Berg, 2007; Amin and Hazelbauer, 2010; Khursigara et al., 2011). Thus, the data presented in Figs 4 and 5 suggest that the intrinsic signal-transduction properties of the cluster, i.e. the dose–response function, can be dynamically modulated by the ligand. This point is depicted schematically in Fig. 6.

Potential implications of cluster dynamics

How can the dynamics of clusters affect signalling under native conditions? We note first that the cluster modulation observed in this study occurs over timescales that are comparable to those measured for receptor methylation (Sourjik and Berg, 2002; Lazova et al., 2011). Because the change in receptor packing is coupled to the primary change in receptor conformation, we expect that receptor packing will also be coupled to the adaptation process of the receptors. Moreover, because the adaptation of each receptor dimer also depends on neighbouring receptors (Li and Hazelbauer, 2005), changes in the packing of the cluster can also affect the intrinsic rates of adaptation. Other factors, such as the accessibility of the adaptation proteins, can also be considered in this context. If the overall effects of clustering on the rates of methylation and demethylation are precisely the same, changes in receptor packing can alter the dynamics of adaptation but not the final equilibrium level of kinase activity. However, if the effects of clustering on the rates of methylation and demethylation are not precisely the same, changes in receptor packing can alter the final steady-state level of kinase activity, leading to deviations from exact adaptation. Such deviations from exact adaptation have been observed in cells that express Tar as the sole chemoreceptor (Meir et al., 2010).

Given the observed changes in the cooperativity of the kinase responses, it is plausible that cluster dynamics could also alter the coupling between receptors of different types. Integration of different signals by the chemoreceptor clusters is an important aspect of chemosensing. Dynamic coupling has recently been invoked in models of mixed clusters to explain the lack of coupling in the steady-state adaptation level of different receptor types (Hansen et al., 2010). In fact, the transient coupling in adaptation observed in such mixed clusters (Lan et al., 2011) occurs over timescales that are comparable to those observed in this study for cluster modulation.

Finally, it has been suggested that Aer-mediated aerotaxis does not involve methylation of Aer and thus does not require the adaptation enzymes (Bibikov et al., 2004; Gosink et al., 2006). Such taxis behaviour could potentially rely on an alternative adaptation mechanism or a process that does not require receptor adaptation (Mazzag et al., 2003). Under such condition in which the primary adaptation mechanism is not effective, dynamical changes in receptor arrays might be a way to modify the transduction properties of the arrays according to external stimuli.

Conclusions

The bacterial chemosensory cluster contains thousands of proteins and is thought to promote signal integration and cooperative responses through direct molecular interactions. Indeed, correlations between receptor clustering and cooperativity have been demonstrated. In this study, we show that changes in clustering and cooperativity occur dynamically in the presence of a stimulus (Fig. 6). Such stimulus-induced modulation in array organization and its effect on signal transduction can be viewed as a plasticity of the chemoreceptor arrays, which can potentially affect various aspects of signalling, such as signal integration, adaptation kinetics and the precision of adaptation.

Experimental procedures

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgements
  8. References
  9. Supporting Information

Bacterial strains and growth conditions

Strains used here are derivatives of the RP437 strain. Strain UU1581 (lacking all chemotaxis genes) donated by Sandy Parkinson (University of Utah), and strain VH1 (tsr, tar, tap, trg, aer, cheY, cheZ, cheR, cheB) donated by Victor Sourjik (University of Heidelberg). The cells were grown overnight in 1 ml of TB (10 g l−1 Bacto Tryptone, 5 g l−1 NaCl), diluted 100-fold in 10 ml TB supplemented with the appropriate antibiotics and inducers and allowed to grow to an OD600 of approximately 0.45, after which they were washed and resuspended in buffer (10 mM potassium phosphate, 0.1 mM EDTA, 1 μM methionine, 10 mM lactic acid, pH 7).

Plasmids

The monomeric variants of YFP (A206K) were used throughout this study. In all of the receptor fusions, YFP was cloned at the C-terminal end via a GlySer(Gly)4Val linker. The C-terminal end of the receptors (29/35 amino acids) consists of a flexible peptide that contains a binding site for the adaptation enzymes (CheR/B). To minimize the possibility of detecting changes in the flexibility or extension of this region alone, we omitted the C-terminal flexible peptide and fused the YFP closer to the trunk of the receptor. Nevertheless, the slow dynamics described here were observed also with the full-length receptor fusions. Vectors carrying tarQQQQ-yfp (pAV45) or tsrQQQQ-yfp (pAV74) and cheY-mcherry-cheZF98S-yfp (pAV76 or pAV109) were described previously (Vaknin and Berg, 2007; Frank et al., 2011). The plasmid pAV45 was induced by 37 μM isopropyl-beta-d-thiogalactopyranoside (IPTG). The plasmids pAV74 and pAV76 were induced by 4 μM IPTG, and the plasmid pAV109 was induced by 2 μM sodium salicylate. The plasmid tarQQQQ-cfp (pAV164) was constructed by replacing yfp with cfp in pAV45 and was induced by 37 μM IPTG.

Fluorescence polarization measurements

Cells were immobilized on a coverslip and mounted in a gold-plated brass flow chamber. The flow chamber was mounted on an aluminium fitting stage in a Nikon FN1 microscope equipped with a 40× Plan-Fluor objective (0.75 NA) and a 150 W xenon lamp (Hamamatsu, Bridgewater, NJ). The YFP proteins were excited with linearly polarized light using a linear glass polarizer (Edmund Optics, Barrington, NJ), an ET508/6× excitation filter (Chroma Technology, Brattleboro, VT), and an FF520Di01 dichroic mirror (Semrock, Rochester, NY). The fluorescence was collected using an FF01-542/27 emission filter (Semrock, Rochester, NY) and split using a polarizing beam-splitter cube (Newport, Irvine, CA). The parallel (Ipar) and perpendicular (Iper) polarizations were monitored with photon counters (H7422P, Hamamatsu, Bridgewater, NJ). The steady-state polarization of the emitted fluorescence was represented by its fluorescence anisotropy r, defined as (Ipar − Iper)/(Ipar + 2Iper), where Iper was corrected for any imperfections in the optical system. By adjusting the anisotropy recorded from an aqueous fluorescein solution to zero, the absolute fluorescence anisotropy level could be determined. This calibration yielded anisotropy of 0.32 for the purified YFP.

Time-lapse fluorescence imaging

Escherichia coli VH1 cells expressing YFP-tagged Tar receptors were immobilized on a coverslip and mounted in a titanium flow chamber. Fluorescence images were obtained using a Nikon Ti inverted microscope equipped with a 100× Plan-Fluor objective (1.3 NA), xenon lamp (Sutter instruments) and camera (Andor technology). Cells were stimulated with α-methylaspartate (1 mM) and the effect on clustering was recorded. Two protocols were used: one, in which cells were cycled between buffer and α-methylaspartate while images are taken 10 min after changing the medium, and second, in which images were taken continually (every minute) documenting the changes upon stimulus. For each cell, the background was subtracted and the maximum intensity and the mean intensity were calculated, and a ‘contrast’ parameter, C, was defined as the ratio between the maximum intensity and the mean intensity minus unity, C = (Imax/Imean) − 1. Changes in this parameter were calculated for each cell as the difference between the average C in α-methylaspartate and its average in buffer, normalized to the average C in buffer. For cells with low contrast (< 2), some enhancement in the contrast could be achieved by repeating the analysis while ignoring pixels that are lower than the mean intensity. Following the procedure described in Frank et al. (2011), the parameter ‘localization’, used in Fig. 3, was calculated as the relative number of pixels in the cell with intensity lower than 30% of the maximum intensity in the cell.

Kinase activity measurements

At a steady state, the kinase activity is proportional to the extent of association between phosphorylated CheY and CheZ. Thus, changes in the kinase activity can be detected by measuring changes in the level of FRET between CheY-mCherry and CheZ-YFP (Sourjik et al., 2007). These changes were detected by exciting the YFP fluorophores, using a 508/6 filter, and by monitoring the ratio, R, between the emissions in the red (630/75 nm) and yellow (550/50 nm) channels. Given the small changes in R (< 10%), the normalized change in kinase activity, which is proportional to the relative change in the number of CheY/CheZ pairs, was evaluated here as 1 − ΔRRmax.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgements
  8. References
  9. Supporting Information

We gratefully thank Sandy Parkinson for providing strains, engaging in discussions and offering helpful comments on the manuscript. We thank Victor Sourjik for strains and helpful discussions. We thank Ned Wingreen for helpful discussions and suggestions. This work was supported by the Israeli Foundation of Sciences and Humanities and the U.S.–Israel Binational Science Foundation.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgements
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Results
  5. Discussion
  6. Experimental procedures
  7. Acknowledgements
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
mmi12215-sup-0001-si.avi23KSupporting Information
mmi12215-sup-0002-si.avi11KSupporting Information
mmi12215-sup-0003-si.pdf2048KSupporting Information

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.