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Characterizing Cross-Talk In Vivo
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Characterizing Cross-Talk In Vivo: Avoiding Pitfalls and Overinterpretation Albert Siryaporn and Mark Goulian Contents 2 3 4 6 8 13 14 14 15 15
1. Overview 2. Sources of Cross-Talk 3. Cross-Talk Suppression 4. Transcriptional Reporters 5. Response Regulator Localization 6. Phosphatase Cross-Talk 7. Signal Response in Cross-Talk Networks 8. Concluding Remarks Acknowledgments References
Abstract Cross-talk between noncognate histidine kinases and response regulators has been widely reported in vitro and, in specific mutant backgrounds and conditions, in vivo. However, in most cases there is little evidence supporting a physiological role of cross-talk. Indeed, histidine kinases and response regulators show remarkable specificity for their cognate partners. In vivo studies of cross-talk have the potential to establish mechanisms that control specificity and, if the cross-talk is observable in wild-type strains, may reveal new levels of cross-regulation. However such studies can be complicated by effects of other regulatory circuits and by the inactivation of mechanisms that would otherwise suppress cross-talk. It is thus easy to mis- or overinterpret the significance of such studies. We address potential complications associated with measuring cross-talk and discuss some methods for identifying and unmasking sources of cross-talk in cells using transcriptional reporters and in vivo DNA-binding assays. Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA Methods in Enzymology, Volume 471 ISSN 0076-6879, DOI: 10.1016/S0076-6879(10)71001-6
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2010 Elsevier Inc. All rights reserved.
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1. Overview When sequence comparisons first revealed the generality of twocomponent signaling, it was immediately appreciated that histidine kinases could potentially phosphorylate noncognate response regulators (Nixon et al., 1986) and that such cross-talk could play a role in signal integration and processing (Stock et al., 1989) (Fig. 1.1). Early studies demonstrated the existence of cross-talk in vitro, although the cross-phosphorylation occurred with considerably slower kinetics compared with phosphorylation between cognate partners (Igo et al., 1989; Ninfa et al., 1988). Cross-talk was also observed in vivo, but only in modified strains in which various regulators were deleted or overexpressed. In the two decades since these early considerations, cross-talk has not emerged as a common theme in two-component signaling. Despite numerous reports in the literature, in most cases there is little evidence to support the claim that there are detectable effects from cross-talk in wild-type strains (Laub and Goulian, 2007). There are several factors that make attempts at identifying cross-talk subject to mis- or overinterpretation. In vitro studies have demonstrated that numerous histidine kinases can phosphorylate noncognate response regulators, with some histidine kinases showing a considerable level of promiscuity (Skerker et al., 2005; Yamamoto et al., 2005). However, as a rule, histidine kinases show a very strong kinetic preference for their cognate response regulator (Skerker et al., 2005). Thus, a demonstration of cross-talk in vitro, without a comparison of the kinetics of phosphotransfer between cognate and noncognate pairs, is not particularly compelling evidence for cross-talk function in vivo (Laub and Goulian, 2007; Skerker et al., 2005). There are additional complications when studying cross-talk in vivo. For most systems, it is not feasible to directly measure response regulator
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Figure 1.1 Cross-talk between noncognate histidine kinases (HKs) and response regulators (RRs) could potentially enable complex processing of multiple input signals. However, to date there has been relatively little evidence for cross-talk in wild-type strains.
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phosphorylation in cells. Instead, phosphorylation is inferred from transcription measurements, e.g., with transcriptional reporters. However, it is often difficult to disentangle the effects of other regulatory factors that may act at the promoter of interest. In addition, studies in mutant strains may identify cross-talk that is irrelevant in the context of wild-type cells. Mechanisms have been identified that suppress cross-talk between noncognate pairs in vivo (Groban et al., 2009; Siryaporn and Goulian, 2008). When these mechanisms are eliminated by mutation, significant cross-talk may emerge (Kim et al., 1996; Silva et al., 1998; Siryaporn and Goulian, 2008), which can give the false impression that cross-talk functions in wild-type strains. Here we describe some of the issues one should take into account when looking for sources or effects of cross-talk. These methods can also be used to explore various mechanisms that limit cross-talk and to explore the molecular determinants of specificity among histidine kinases and response regulators. This chapter covers potential sources of cross-talk, cross-talk suppression mechanisms, and methods for measuring cross-talk in cells. In particular, it provides specific details on how to measure the association of response regulator-fluorescent protein fusions to DNA in vivo, a method which can distinguish cross-talk from the effects of other regulatory pathways. We also briefly discuss methods for characterizing phosphatase crosstalk and the effects of signal response on cross-talk networks.
2. Sources of Cross-Talk A response regulator can potentially be phosphorylated by a cognate histidine kinase, noncognate histidine kinases, and small molecule phosphodonors. It is generally assumed that when genes for a response regulator and a histidine kinase are in the same operon, the encoded proteins are cognate pairs. Situations in which more than one histidine kinase or response regulator is encoded in an operon, however, may be more complex. It is also not unusual to encounter ‘‘orphan’’ histidine kinases and response regulators for which no clear partner has been identified. Many microbial genomes appear to have unequal numbers of histidine kinases and response regulators (Alm et al., 2006), which suggests that some response regulators may have more than one source of phosphorylation and similarly some histidine kinases may phosphorylate multiple response regulators. In some systems, the histidine kinase is bifunctional and mediates response regulator phosphorylation and dephosphorylation. In this case, there could in principle be cross-talk from either the phosphotransfer or phosphatase activities. However, we are unaware of any reports of phosphatase cross-talk, even in mutant strains where phosphotransfer cross-talk has been observed. For example, the histidine kinase CpxA phosphorylates
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and dephosphorylates its cognate response regulator CpxR. Cross-talk from CpxA to OmpR can be detected in strains deleted for envZ and cpxR, however, dephosphorylation of OmpR-P by CpxA is not detected (Siryaporn and Goulian, 2008). Kinetic modeling of the histidine kinase– response regulator interaction suggests that phosphatase activity may emerge for sufficiently strong interaction between a bifunctional histidine kinase and response regulator (Siryaporn et al., in preparation). Thus, the difficulty in observing phosphatase cross-talk may reflect the relatively weak interactions between noncognate partners. A number of response regulators are phosphorylated by the small molecule phosphodonor acetyl phosphate (McCleary and Stock, 1994; Wanner and Wilmes-Riesenberg, 1992; Wolfe, 2005). The cellular concentration of this high-energy source of phosphoryl groups is strongly dependent on growth conditions, such as carbon source (Wolfe, 2005). Concentrations can be quite high in some cases, which raises the possibility that acetyl phosphate may integrate metabolic status into some two-component systems (Fredericks et al., 2006; Klein et al., 2007; Wanner, 1992). For most two-component systems, however, a role for acetyl phosphate has not been established. There is also the possibility that other small molecule phosphodonors (Lukat et al., 1992) could function to phosphorylate response regulators in some contexts in vivo. The ability of a phosphodonor to affect the steady-state phosphorylation level of a particular response regulator depends on the rate of the phosphorylation reaction relative to the rates of other sources of phosphorylation and dephosphorylation. While phosphorylation rates of cytoplasmic fragments of histidine kinases are readily measured in vitro using purified components (Laub et al., 2007), the effects of individual phosphorylation sources on levels of phosphorylated response regulator in vivo can be quite sensitive to the strain background and can be difficult to measure. For example, a bifunctional histidine kinase that has high rates of phosphorylation and dephosphorylation can give levels of phosphorylated response regulator that are comparable to those that would arise from a histidine kinase that has weak phosphorylation activity but lacks a mechanism for response regulator dephosphorylation (Fig. 1.2A). However, through a careful and systematic analysis using transcriptional and translational reporter fusions, the contributions from different phosphorylation sources can in some cases be disentangled.
3. Cross-Talk Suppression Studies have shown that cross-talk can be suppressed by cognate histidine kinases and response regulators (Fig. 1.2B and C) (Groban et al., 2009; Kim et al., 1996; Silva et al., 1998; Siryaporn and Goulian, 2008).
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Figure 1.2 (A) Two different scenarios in which very different rates of phosphorylation can give the same steady state level of phosphorylated response regulator (RR-P). (Left) A bifunctional histidine kinase phosphorylates and dephosphorylates a response regulator with very high rates such that, under moderate stimulation, a moderate level of RR-P is obtained at steady state. (Right) A histidine kinase that phosphorylates a response regulator very weakly may, in the absence of any phosphatase activity, also produce moderate levels of RR-P. (B, C) Suppression of cross-talk from a histidine kinase HK2 to response regulator RR1 by cognate partners (Groban et al., 2009; Siryaporn and Goulian, 2008). (B) The high flux of phosphorylation and dephosphorylation of RR1 by the bifunctional histidine kinase HK1 can suppress or buffer against cross-talk from HK2. (C) Suppression of cross-talk by a cognate response regulator. The cognate response regulator RR2 outcompetes the noncognate regulator RR1 for interaction with the histidine kinase HK2. By removing RR2, significant cross-talk from HK2 to RR1 may be detected, which would be otherwise absent in the wild-type strain.
Mutations that relieve these suppression mechanisms could produce signals of cross-talk that would otherwise be squelched in the wild-type strain. To avoid over- or misinterpreting the results from analysis of mutant strains, it is important to take into account the potential effects of these mechanisms.
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Bifunctional histidine kinases set the level of phosphorylated response regulator through a balance of phosphorylation and dephosphorylation. This cycle can effectively act as a buffer to suppress the effects of weaker phosphorylation sources (Fig. 1.2B) (Groban et al., 2009; Kim et al., 1996; Silva et al., 1998; Siryaporn and Goulian, 2008). Therefore, deletion of a bifunctional histidine kinase could reveal phosphorylation of the response regulator from a noncognate histidine kinase or other phosphodonor (e.g., Batchelor et al., 2005; Danese and Silhavy, 1998; Hutchings et al., 2006; Wolfe et al., 2008). However, this alternate source may give a much lower flux of phosphoryl groups and may thus be physiologically irrelevant in wild-type strains. A response regulator can also prevent its cognate histidine kinase from participating in cross-talk (Fig. 1.2C). For example, cross-talk from CpxA to OmpR is suppressed by CpxR (Siryaporn and Goulian, 2008). This is likely to occur through competitive inhibition, in which the cognate response regulator outcompetes noncognate response regulators for interaction with the histidine kinase. Thus, deletion or inactivation of a response regulator could potentially produce cross-talk from the cognate histidine kinase to some other response regulator. Significant overexpression of the histidine kinase could also have a similar result.
4. Transcriptional Reporters In most two-component systems that have been studied, the phosphorylated response regulator controls transcription of effecter genes. Therefore, transcriptional reporter fusions have been a standard tool for measuring the output of signal transduction systems. A reporter gene such as gfp or lacZ is placed under control of a promoter of interest and expression of the reporter is measured through fluorescence or enzyme assays. Cross-talk can be measured using transcriptional reporter fusions. Chromosomal fusions are preferable to constructs on multicopy plasmids since they avoid potential effects on reporter gene expression from titrating out response regulator and changes in plasmid copy number. It is always a good idea to assess the dynamic range of reporter expression since this gives an indication of the sensitivity to changes in output under various conditions. The basal expression level can be determined from measurements in a strain deleted for the response regulator of interest. The range of activation can be determined by making measurements for different levels of input stimulus, if the stimulus is known, or by varying expression of a constitutively active version of the histidine kinase, if one is available. For experiments in which a fluorescent reporter such as gfp is used, it is preferable to grow cultures using a growth medium with a low level of autofluorescence such as a
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minimal salts medium (Miller, 1992), as this generally increases the sensitivity of measurements. Rich media often give moderate levels of autofluorescence, which could reduce the sensitivity of fluorescence measurements. Evidence for the presence and absence of cross-talk can be followed by monitoring changes in transcriptional reporter expression for different genetic backgrounds and conditions. Depending on the application, it may be necessary to remove cross-talk suppression mechanisms described above by deleting appropriate histidine kinases and response regulators before cross-talk can be detected. The observation of cross-talk in this case should not be used to conclude that there is cross-talk in the wild-type strain. Specific histidine kinases that are potential sources of cross-talk can be tested by changing expression levels or stimulation with signal. However, their effects on cross-talk will depend on details of the histidine kinase and its interaction with the noncognate response regulator, as discussed below. A histidine kinase sets the level of phosphorylated response regulator through phosphorylation alone (monofunctional behavior) or through a balance of phosphorylation and dephosphorylation (bifunctional behavior). For histidine kinases displaying monofunctional behavior, the level of phosphorylated response regulator is expected to be quite sensitive to histidine kinase expression level (E. Batchelor and M. Goulian, unpublished observations; Miyashiro and Goulian, 2008). On the other hand, when the histidine kinase has bifunctional behavior, modeling and experiments suggest the level of phosphorylated response regulator can be relatively insensitive to histidine kinase expression levels (Batchelor and Goulian, 2003; Miyashiro and Goulian, 2008; Shinar et al., 2007). Thus, cross-talk may be observed in a context of histidine kinase overexpression even though it appears that cognate response regulator phosphorylation is unchanged. In addition, in at least some cases, a histidine kinase can exhibit bifunctional behavior against its cognate partner while displaying monofunctional behavior against a noncognate partner (Siryaporn and Goulian, 2008). The choice of carbon source may have a significant effect on the level of acetyl phosphate in the cell (McCleary and Stock, 1994) and thus the level of response regulator phosphorylation. The desired level of acetyl phosphate may depend on the experimental condition being tested. In some cases, significant levels of acetyl phosphate may provide a means of increasing the basal level of response regulator phosphorylation and hence increase the sensitivity to cross-talk by other phosphodonors. In other cases, one may want to eliminate acetyl phosphate or keep its level to a minimum in order to distinguish its effects from other sources of response regulator phosphorylation. For Escherichia coli, growth using carbon sources such as glucose, pyruvate, and acetate produce high levels of acetyl phosphate. Low levels can be achieved by growing on glycerol. One can also modulate acetyl phosphate levels by deleting one or both of pta and ackA, depending on the flux of acetate through the Pta (phosphotransacetylase)–AckA (acetate
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kinase) pathway (Wolfe et al., 2008). Deletion of pta ackA abolishes production of acetyl phosphate. However, care should be taken in altering the Pta–AckA pathway as this may have additional indirect effects on some two-component systems (Wolfe et al., 2008). While transcriptional reporter fusions can provide effective measurements of two-component system output, they are also subject to the effects of other regulatory factors. There are many examples of genes whose expression is controlled by multiple regulatory proteins and small RNAs. Even many relatively well-characterized systems may have additional unidentified regulators whose effects could give the false impression of cross-talk. In some cases, promoters may also be controlled by multiple response regulators (e.g., Batchelor et al., 2005; Mouslim and Groisman, 2003). To distinguish between cross-talk and alternate regulatory pathways, one may need to use additional methods to follow response regulator phosphorylation or binding to promoters.
5. Response Regulator Localization If a functional fluorescent protein (FP) fusion to the response regulator of interest is available then one can use a fluorescence localization assay to provide further support for cross-talk in vivo (Batchelor and Goulian, 2006; Siryaporn and Goulian, 2008). The method takes advantage of the tendency of plasmids with partitioning systems to form clusters in the cell (Pogliano, 2002). In this assay, cells express the response regulator-FP fusion and also contain a plasmid that has one or more binding sites for the response regulator. Plasmid clustering provides a high local density of response regulator binding sites in the cell (Fig. 1.3A). Significant response regulator-FP binding to DNA results in intense localized regions of fluorescence, which appear as bright foci or spots under a fluorescence microscope and can be easily quantified by image analysis (Fig. 1.3B–D). Assuming DNA binding is modulated by response regulator phosphorylation, changes in response regulator-FP colocalization with the plasmid can be used to infer changes in response regulator phosphorylation. We have been able to construct functional fluorescent protein fusions to the C-termini of several different response regulators in E. coli, although in all of the cases that we tested the fusions showed decreased activity. We are unaware of any general rules regarding the choice of C- or N-termini for the fusion, or whether or not to include a flexible linker. It is probably best to try several different constructs. For the plasmid, we have used constructs based on pRS415 (Batchelor and Goulian, 2006; Simons et al., 1987), which is derived from pBR322. Many other plasmids should work as well although they may show differing levels of clustering. Plasmid clustering can
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Figure 1.3 Measurement of response regulator binding to DNA in vivo. (A) Schematic of the method, which uses a functional translational fusion of a fluorescent protein (e.g., YFP) to the response regulator and a plasmid containing response regulator binding sites. The plasmid also contains lac operators, which are bound by CFP-LacI. Plasmid clustering in the cell results in a high local concentration of binding sites, which is easily visualized as a bright fluorescent spot in the CFP channel. Response regulator binding to plasmid also results in a bright fluorescent spot in the YFP channel. (B) Images of cells expressing OmpR-YFP and containing a plasmid with OmpR binding sites (top row) or an empty control vector (bottom row). DIC—differential interference contrast image. (C) Example of a YFP spot, corresponding to the dashed square in the upper right image in (B), and the associated pixel values and distances from the center. (D) Gaussian fit to the profile of pixel values from (C). A corresponding fit to the neighborhood of a maximal YFP pixel in a cell containing the vector control is also shown.
be characterized through the use of lac operators and a fluorescent protein fusion to lac repressor, as discussed below. The ability to detect fluorescent protein localization to plasmids will depend on the binding affinity of the
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phosphorylated response regulator-FP fusion as well as the extent of phosphorylation. One can increase the sensitivity by including several binding sites in the plasmid as needed. It is generally advisable to include a means to monitor plasmid clustering independently of response regulator binding to DNA. This controls for potential changes in plasmid clustering as a result of changes in cell physiology. This is easily accomplished by cloning lac operators into the plasmid and expressing a fluorescent protein fusion to lac repressor. We routinely use a CFP-LacI fusion (Batchelor and Goulian, 2006), which can be simultaneously imaged with a response regulator-YFP fusion. We have used an array of tandem lac operator repeats (Robinett et al., 1996) to mark the plasmid. This produces extremely bright foci, which is convenient as it provides increased sensitivity and enables shorter exposures when acquiring images. After plasmid clusters have been identified in the CFP channel, one can then quantify the YFP fluorescence in the neighborhood of this location. Under conditions of very high response regulator phosphorylation one may observe additional foci that are not associated with plasmids. In at least one case (OmpR-YFP), we have determined that these foci are due to response regulator binding to chromosomal loci (E. Libby and M. Goulian, manuscript in preparation). Fluorescence localization can be visualized using techniques adapted from general single-cell fluorescence microscopy methods (e.g., Batchelor and Goulian, 2006; Miyashiro and Goulian, 2007). 1. Grow cultures in medium with aeration at the appropriate growth temperature to saturation. To maximize the sensitivity of this assay, a culture medium that gives relatively low levels of autofluorescence should be used. A minimal salts medium is ideal in this respect and also allows for choice of carbon source. 2. Dilute cultures at least 1:1000 into the same medium and grow to early exponential phase (optical density of 0.1–0.3 at 600 nm for E. coli). 3. Shortly before cells reach the target density, preheat the microscope stage to the same temperature as that used for growing the cell cultures. Prepare 1% agarose pads made from the same growth medium and placed between a microscope slide and cover glass. For many smallscale experiments, it is sufficient to use 3 in. 1 in. 1 mm slides and 22 mm square #1.5 cover glass. Place 50–100 ml of molten agarose on the center of slide and cover immediately with coverslip. Agarose pads should be maintained at the growth temperature of the culture and may need time to dry slightly before use. Cells will not immobilize well on agarose pads that are too moist. 4. Carefully lift the cover glass off of the pad and place 5 ml of culture onto cover glass. Replace cover glass back onto agarose pad with culture between the cover glass and pad and put the slide on the
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microscope stage immediately. The number of cells on the agarose pad should be such that cells are easily found in most fields but are in sufficiently low density that there is little or no contact between neighboring cells. Obtaining proper cell density in images is especially important for the fluorescence analysis that follows. 5. Acquire a phase contrast or differential interference contrast (DIC) image and fluorescence images of the same field on a fluorescence microscope (Fig. 1.3B). Image acquisition times should be long enough so that cell fluorescence is significantly higher than background but brief enough so that none of the pixel values is equal to the maximum allowed value. The maximum pixel values are 255, 1023, 4095, and 65355 for 8-, 10- 12-, and 16-bit images, respectively. Objective magnification and resolution of the digital camera should be sufficient so that the shortest dimension of individual cells is at least approximately 5 pixels. Images should be saved without applying any contrast enhancements or other processing. Cell fluorescence levels may be relatively weak for response regulator fusions that are expressed at wild-type levels. In addition, fluorescent foci are not always observable through the eyepiece and may photo-bleach within seconds. It is important to acquire images from fields of cells that have not been previously exposed to light from the fluorescence illuminator. In particular, cells should be brought into focus using phase or DIC imaging. The extent of fluorescence localization will vary from cell to cell due to fluctuations in protein expression levels, plasmid copy number, plasmid localization, or other variations in the cellular environment. A representative measure of fluorescence localization for the population can be attained by acquiring images of a large number of cells (e.g., 100–200) and quantifying the extent of localization as described below. While dramatic changes in fluorescence localization may be sufficient to support a particular conclusion, in some cases it may be necessary to distinguish smaller changes in localization that are not easily assessed by eye. The fluorescence localization of cell populations can be quantified from images using software packages such as ImageJ (National Institutes of Health, Bethesda, MD), LabVIEW (National Instruments, Austin, TX), and MATLAB (The MathWorks, Natick, MA), which provide convenient libraries of imaging tools. Modules and plug-ins provided by these packages can be stitched together through scripts or macros to process large numbers of images quickly and accurately. The basic assumption behind this analysis is that fluorescent foci can be accurately represented by point-source intensity profiles. The maximum value of the fluorescence in the cell or the maximum value in the neighborhood of the plasmid cluster is taken to be the peak of the fluorescence intensity profile. Pixel values surrounding this maximum are extracted, assigned pixel distances, and fitted to a Gaussian function. Numerical
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parameters derived from the best-fit intensity profiles are then used to compute an integrated fluorescence intensity, which is a measure of the extent of fluorescence localization. This method assumes that YFP fluorescence and phase contrast or DIC images have been collected. Instructions also describe cases where the investigator has chosen to express CFP-LacI to identify plasmid clusters as described above. 1. A phase contrast or DIC image and a YFP fluorescence image should be available for analysis. If the CFP-LacI system was also used, then a CFP fluorescence image should also be on hand. 2. Determine cell boundaries using the phase or DIC image. Edge detection algorithms, such as the Sobel edge filter (Castleman, 1996), can be used for identifying cell boundaries in phase contrast images. For images which contain relatively low or complex contrast, such as images from DIC microscopy, cell boundaries can be identified by thresholding pixel values (Batchelor and Goulian, 2006; Miyashiro and Goulian, 2007). 3. Once cell boundaries are determined, it is often convenient to construct a binary (black and white) mask, which defines areas where cells are present. Masks can be constructed from cell boundary images by filling in regions marked by the cell boundaries. 4. Extract pixel values from corresponding fluorescence images using the mask as a guide for cell locations. It is convenient to extract pixel values for individual cells so that each cell can be processed individually from this point on. 5. Determine the location of the plasmid cluster within the cell as follows: (a) If a CFP-LacI image marking plasmid clusters has been acquired, the brightest pixel within each cell boundary in the CFP image should be identified. If the response regulator-YFP is colocalized with the plasmid cluster, it should be observable in the vicinity of the LacICFP peak. Identify the location of the maximum YFP peak within the local pixel neighborhood of the CFP peak. For example, for a cell that is represented by approximately 500 pixels, one might choose to restrict the search area to be within a 3 3 pixel area. This maximum YFP value is taken to be the peak of the YFP (response regulator) intensity profile. (b) If a CFP-LacI image is not available, identify the location of the maximum pixel value within the cell boundary of the YFP (response regulator) image. This is taken to be the peak of the YFP (response regulator) intensity profile. 6. Extract pixel values surrounding the peak of the YFP intensity profile (Fig. 1.3B). The extracted area should be large enough to encompass a section representative of the spot and should not include areas outside of the cell. Assign a radial distance to each pixel value with the peak pixel value as the center (Fig. 1.3C).
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7. Using a nonlinear fitting algorithm, such as the Levenberg-Marquardt algorithm (Press and Numerical Recipes Software (Firm), 1992), fit the pixel intensities I as a function of radial distance r to the Gaussian form I = A exp(Br2) þ C with fitting parameters A, B, and C (Fig. 1.3D). To compute the integrated peak fluorescence intensity, one can take the integral of the Gaussian using parameters for the best fit. One can also fit to a Gaussian that is not radially symmetric as described in Siryaporn and Goulian (2008). This has the advantage that it allows for asymmetric patterns of fluorescence in the neighborhood of fluorescence maxima near the boundary of the cell. The parameters describe intensity characteristics of the peak profile: A characterizes the peak intensity value, the inverse of B characterizes the width of the spot, and C characterizes the local background fluorescence. We have found the parameter A as well as the integral of the Gaussian over the fitting area can both provide reasonable measures of response regulatorYFP colocalization with plasmids. In some cases, the fits may give very poor representations of the fluorescence distribution profile. Obviously poor fits (e.g., negative parameters for A or C) can be thrown out using a predetermined cut-off criterion. When analyzing cell images, it is important to spotcheck parameters from individual cells and compare these to actual locations in the cell to verify that the program is working properly and that parameters accurately describe characteristics of the fluorescence profile.
6. Phosphatase Cross-Talk The effects of phosphatase cross-talk in principle should be readily detectable as a decrease in response regulator phosphorylation. However, the mechanisms described above will likely suppress weak phosphatase cross-talk in the same way that phosphorylation cross-talk is suppressed. To look for possible signals of phosphatase activity, it is therefore useful to establish a system for response regulator phosphorylation in the absence of the wild-type cognate histidine kinase. For some response regulators, this can be accomplished by growing cells under conditions of high acetyl phosphate (e.g., growth on glucose or pyruvate). Another approach is to use a mutant of the cognate histidine kinase that lacks phosphatase activity but retains kinase activity. Many of these kinaseþ phosphatase- mutants appear to function as relatively weak kinases. However, they nevertheless produce high levels of response regulator phosphorylation because of the absence of a mechanism for rapid response regulator dephosphorylation. If the histidine kinase being tested does show evidence of dephosphorylation activity against the noncognate response regulator, this is at least consistent with some level of phosphatase cross-talk. Of course all of the caveats
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described above regarding the relevance for wild-type cells still apply. In addition, one must consider the possibility that the decreased phosphorylation is due to an indirect effect, for example, from decreased response regulator expression, decreased expression of the (kinaseþ phosphatase-) mutant, or decreased acetyl phosphate levels.
7. Signal Response in Cross-Talk Networks Most histidine kinases that have been studied are associated with signal detection; they modulate the level of phosphorylated response regulator in response to changes in signal. In general, the signal could affect the rate of autophosphorylation or the rate of response regulator dephosphorylation. In some cases, modulation of the input signal for a specific histidine kinase could provide evidence for cross-talk to a noncognate response regulator. However, the interpretation of these results should be viewed with caution if other physiological effects of the input signal are not well understood. Indeed, the signals for many histidine kinases, such as osmolarity, unfolded proteins, pH, variations in specific ions, etc. are likely to have many effects on the cell through pathways distinct from the specific histidine kinase that one is attempting to stimulate. It is also quite possible that cross-talk is present but is blind to signal. Interestingly, this appears to be the case in some examples where cross-talk has been characterized (Silva et al., 1998; Siryaporn and Goulian, 2008). One model suggests that this is due to the relatively weak interactions between the noncognate histidine kinase and response regulator (Siryaporn et al., in preparation).
8. Concluding Remarks The level of phosphorylated response regulator is generally expected to be sensitive to perturbations in the rates of the phosphorylation and dephosphorylation reactions. Most wild-type circuits appear to be designed to protect against such sensitivity. However, in mutant backgrounds, in which cognate histidine kinases and response regulators have been deleted, sensitivity can emerge, resulting in cross-talk from noncognate histidine kinases or alternative phosphodonors. This can be useful for studying the specificity of histidine kinase–response regulator interactions, but does not necessarily indicate a physiological role of cross-talk, or even the existence of cross-talk, in wild-type cells. Indirect effects on histidine kinase or response regulator expression level, the level of other phosphodonors, or possibly other regulators can also give an easily misinterpreted signal of cross-talk in mutant backgrounds. Nevertheless, through a careful and
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systematic dissection of two-component networks, the level of insulation, and flow of phosphorylation between individual components can be disentangled from the complex web of interacting regulatory pathways.
ACKNOWLEDGMENTS The work was supported by NIH grant R01GM080279 to M. G. A. S. was also supported by the NIH Bacteriology Training Grant T32 AI060516.
REFERENCES Alm, E., Huang, K., et al. (2006). The evolution of two-component systems in bacteria reveals different strategies for niche adaptation. PLoS Comput. Biol. 2(11), e143. Batchelor, E., and Goulian, M. (2003). Robustness and the cycle of phosphorylation and dephosphorylation in a two-component regulatory system. Proc. Natl. Acad. Sci. USA 100(2), 691–696. Batchelor, E., and Goulian, M. (2006). Imaging OmpR localization in Escherichia coli. Mol. Microbiol. 59(6), 1767–1778. Batchelor, E., Walthers, D., et al. (2005). The Escherichia coli CpxA-CpxR envelope stress response system regulates expression of the porins ompF and ompC. J. Bacteriol. 187(16), 5723–5731. Castleman, K. R. (1996). Digital Image Processing. Upper Saddle River, Prentice Hall, NJ. Danese, P. N., and Silhavy, T. J. (1998). CpxP, a stress-combative member of the Cpx regulon. J. Bacteriol. 180(4), 831–839. Fredericks, C. E., Shibata, S., et al. (2006). Acetyl phosphate-sensitive regulation of flagellar biogenesis and capsular biosynthesis depends on the Rcs phosphorelay. Mol. Microbiol. 61(3), 734–747. Groban, E. S., Clarke, E. J., et al. (2009). Kinetic buffering of cross talk between bacterial two-component sensors. J. Mol. Biol. 390(3), 380–393. Hutchings, M. I., Hong, H. J., et al. (2006). The vancomycin resistance VanRS twocomponent signal transduction system of Streptomyces coelicolor. Mol. Microbiol. 59(3), 923–935. Igo, M. M., Ninfa, A. J., et al. (1989). Phosphorylation and dephosphorylation of a bacterial transcriptional activator by a transmembrane receptor. Genes Dev. 3(11), 1725–1734. Kim, S. K., Wilmes-Riesenberg, M. R., et al. (1996). Involvement of the sensor kinase EnvZ in the in vivo activation of the response-regulator PhoB by acetyl phosphate. Mol. Microbiol. 22(1), 135–147. Klein, A. H., Shulla, A., et al. (2007). The intracellular concentration of acetyl phosphate in Escherichia coli is sufficient for direct phosphorylation of two-component response regulators. J. Bacteriol. 189(15), 5574–5581. Laub, M. T., and Goulian, M. (2007). Specificity in two-component signal transduction pathways. Annu. Rev. Genet. 41, 121–145. Laub, M. T., Biondi, E. G., et al. (2007). Phosphotransfer profiling: Systematic mapping of two-component signal transduction pathways and phosphorelays. Methods Enzymol. 423, 531–548. Lukat, G. S., McCleary, W. R., et al. (1992). Phosphorylation of bacterial response regulator proteins by low molecular weight phospho-donors. Proc. Natl. Acad. Sci. USA 89(2), 718–722.
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McCleary, W. R., and Stock, J. B. (1994). Acetyl phosphate and the activation of twocomponent response regulators. J. Biol. Chem. 269(50), 31567–31572. Miller, J. H. (1992). A Short Course in Bacterial Genetics: A Laboratory Manual and Handbook for Escherichia coli and Related Bacteria. Cold Spring Harbor Laboratory Press, Plainview, NY. Miyashiro, T., and Goulian, M. (2007). Single-cell analysis of gene expression by fluorescence microscopy. Methods Enzymol. 423, 458–475. Miyashiro, T., and Goulian, M. (2008). High stimulus unmasks positive feedback in an autoregulated bacterial signaling circuit. Proc. Natl. Acad. Sci. USA 105(45), 17457–17462. Mouslim, C., and Groisman, E. A. (2003). Control of the Salmonella ugd gene by three twocomponent regulatory systems. Mol. Microbiol. 47(2), 335–344. Ninfa, A. J., Ninfa, E. G., et al. (1988). Crosstalk between bacterial chemotaxis signal transduction proteins and regulators of transcription of the Ntr regulon: Evidence that nitrogen assimilation and chemotaxis are controlled by a common phosphotransfer mechanism. Proc. Natl. Acad. Sci. USA 85(15), 5492–5496. Nixon, B. T., Ronson, C. W., et al. (1986). Two-component regulatory systems responsive to environmental stimuli share strongly conserved domains with the nitrogen assimilation regulatory genes ntrB and ntrC. Proc. Natl. Acad. Sci. USA 83(20), 7850–7854. Pogliano, J. (2002). Dynamic cellular location of bacterial plasmids. Curr. Opin. Microbiol. 5(6), 586–590. Press, W. H., and Numerical Recipes Software (Firm) (1992). Numerical recipes in C. Cambridge [England]. Cambridge University Press, New York. Robinett, C. C., Straight, A., et al. (1996). In vivo localization of DNA sequences and visualization of large-scale chromatin organization using lac operator/repressor recognition. J. Cell. Biol. 135(6 Pt 2), 1685–1700. Shinar, G., Milo, R., et al. (2007). Input output robustness in simple bacterial signaling systems. Proc. Natl. Acad. Sci. USA 104(50), 19931–19935. Silva, J. C., Haldimann, A., et al. (1998). In vivo characterization of the type A and B vancomycin-resistant enterococci (VRE) VanRS two-component systems in Escherichia coli: A nonpathogenic model for studying the VRE signal transduction pathways. Proc. Natl. Acad. Sci. USA 95(20), 11951–11956. Simons, R. W., Houman, F., et al. (1987). Improved single and multicopy lac-based cloning vectors for protein and operon fusions. Gene 53(1), 85–96. Siryaporn, A., and Goulian, M. (2008). Cross-talk suppression between the CpxA-CpxR and EnvZ-OmpR two-component systems in E. coli. Mol. Microbiol. 70(2), 494–506. Skerker, J. M., Prasol, M. S., et al. (2005). Two-component signal transduction pathways regulating growth and cell cycle progression in a bacterium: A system-level analysis. PLoS Biol. 3(10), e334. Stock, J. B., Ninfa, A. J., et al. (1989). Protein phosphorylation and regulation of adaptive responses in bacteria. Microbiol. Rev. 53(4), 450–490. Wanner, B. L. (1992). Is cross regulation by phosphorylation of two-component response regulator proteins important in bacteria? J. Bacteriol. 174(7), 2053–2058. Wanner, B. L., and Wilmes-Riesenberg, M. R. (1992). Involvement of phosphotransacetylase, acetate kinase, and acetyl phosphate synthesis in control of the phosphate regulon in Escherichia coli. J. Bacteriol. 174(7), 2124–2130. Wolfe, A. J. (2005). The acetate switch. Microbiol. Mol. Biol. Rev. 69(1), 12–50. Wolfe, A. J., Parikh, N., et al. (2008). Signal integration by the two-component signal transduction response regulator CpxR. J. Bacteriol. 190(7), 2314–2322. Yamamoto, K., Hirao, K., et al. (2005). Functional characterization in vitro of all twocomponent signal transduction systems from Escherichia coli. J. Biol. Chem. 280(2), 1448–1456.
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Characterizing Cross-Talk In Vivo: Avoiding Pitfalls and Overinterpretation Albert Siryaporn and Mark Goulian Contents 2 3 4 6 8 13 14 14 15 15
1. Overview 2. Sources of Cross-Talk 3. Cross-Talk Suppression 4. Transcriptional Reporters 5. Response Regulator Localization 6. Phosphatase Cross-Talk 7. Signal Response in Cross-Talk Networks 8. Concluding Remarks Acknowledgments References
Abstract Cross-talk between noncognate histidine kinases and response regulators has been widely reported in vitro and, in specific mutant backgrounds and conditions, in vivo. However, in most cases there is little evidence supporting a physiological role of cross-talk. Indeed, histidine kinases and response regulators show remarkable specificity for their cognate partners. In vivo studies of cross-talk have the potential to establish mechanisms that control specificity and, if the cross-talk is observable in wild-type strains, may reveal new levels of cross-regulation. However such studies can be complicated by effects of other regulatory circuits and by the inactivation of mechanisms that would otherwise suppress cross-talk. It is thus easy to mis- or overinterpret the significance of such studies. We address potential complications associated with measuring cross-talk and discuss some methods for identifying and unmasking sources of cross-talk in cells using transcriptional reporters and in vivo DNA-binding assays. Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA Methods in Enzymology, Volume 471 ISSN 0076-6879, DOI: 10.1016/S0076-6879(10)71001-6
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2010 Elsevier Inc. All rights reserved.
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1. Overview When sequence comparisons first revealed the generality of twocomponent signaling, it was immediately appreciated that histidine kinases could potentially phosphorylate noncognate response regulators (Nixon et al., 1986) and that such cross-talk could play a role in signal integration and processing (Stock et al., 1989) (Fig. 1.1). Early studies demonstrated the existence of cross-talk in vitro, although the cross-phosphorylation occurred with considerably slower kinetics compared with phosphorylation between cognate partners (Igo et al., 1989; Ninfa et al., 1988). Cross-talk was also observed in vivo, but only in modified strains in which various regulators were deleted or overexpressed. In the two decades since these early considerations, cross-talk has not emerged as a common theme in two-component signaling. Despite numerous reports in the literature, in most cases there is little evidence to support the claim that there are detectable effects from cross-talk in wild-type strains (Laub and Goulian, 2007). There are several factors that make attempts at identifying cross-talk subject to mis- or overinterpretation. In vitro studies have demonstrated that numerous histidine kinases can phosphorylate noncognate response regulators, with some histidine kinases showing a considerable level of promiscuity (Skerker et al., 2005; Yamamoto et al., 2005). However, as a rule, histidine kinases show a very strong kinetic preference for their cognate response regulator (Skerker et al., 2005). Thus, a demonstration of cross-talk in vitro, without a comparison of the kinetics of phosphotransfer between cognate and noncognate pairs, is not particularly compelling evidence for cross-talk function in vivo (Laub and Goulian, 2007; Skerker et al., 2005). There are additional complications when studying cross-talk in vivo. For most systems, it is not feasible to directly measure response regulator
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Figure 1.1 Cross-talk between noncognate histidine kinases (HKs) and response regulators (RRs) could potentially enable complex processing of multiple input signals. However, to date there has been relatively little evidence for cross-talk in wild-type strains.
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phosphorylation in cells. Instead, phosphorylation is inferred from transcription measurements, e.g., with transcriptional reporters. However, it is often difficult to disentangle the effects of other regulatory factors that may act at the promoter of interest. In addition, studies in mutant strains may identify cross-talk that is irrelevant in the context of wild-type cells. Mechanisms have been identified that suppress cross-talk between noncognate pairs in vivo (Groban et al., 2009; Siryaporn and Goulian, 2008). When these mechanisms are eliminated by mutation, significant cross-talk may emerge (Kim et al., 1996; Silva et al., 1998; Siryaporn and Goulian, 2008), which can give the false impression that cross-talk functions in wild-type strains. Here we describe some of the issues one should take into account when looking for sources or effects of cross-talk. These methods can also be used to explore various mechanisms that limit cross-talk and to explore the molecular determinants of specificity among histidine kinases and response regulators. This chapter covers potential sources of cross-talk, cross-talk suppression mechanisms, and methods for measuring cross-talk in cells. In particular, it provides specific details on how to measure the association of response regulator-fluorescent protein fusions to DNA in vivo, a method which can distinguish cross-talk from the effects of other regulatory pathways. We also briefly discuss methods for characterizing phosphatase crosstalk and the effects of signal response on cross-talk networks.
2. Sources of Cross-Talk A response regulator can potentially be phosphorylated by a cognate histidine kinase, noncognate histidine kinases, and small molecule phosphodonors. It is generally assumed that when genes for a response regulator and a histidine kinase are in the same operon, the encoded proteins are cognate pairs. Situations in which more than one histidine kinase or response regulator is encoded in an operon, however, may be more complex. It is also not unusual to encounter ‘‘orphan’’ histidine kinases and response regulators for which no clear partner has been identified. Many microbial genomes appear to have unequal numbers of histidine kinases and response regulators (Alm et al., 2006), which suggests that some response regulators may have more than one source of phosphorylation and similarly some histidine kinases may phosphorylate multiple response regulators. In some systems, the histidine kinase is bifunctional and mediates response regulator phosphorylation and dephosphorylation. In this case, there could in principle be cross-talk from either the phosphotransfer or phosphatase activities. However, we are unaware of any reports of phosphatase cross-talk, even in mutant strains where phosphotransfer cross-talk has been observed. For example, the histidine kinase CpxA phosphorylates
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and dephosphorylates its cognate response regulator CpxR. Cross-talk from CpxA to OmpR can be detected in strains deleted for envZ and cpxR, however, dephosphorylation of OmpR-P by CpxA is not detected (Siryaporn and Goulian, 2008). Kinetic modeling of the histidine kinase– response regulator interaction suggests that phosphatase activity may emerge for sufficiently strong interaction between a bifunctional histidine kinase and response regulator (Siryaporn et al., in preparation). Thus, the difficulty in observing phosphatase cross-talk may reflect the relatively weak interactions between noncognate partners. A number of response regulators are phosphorylated by the small molecule phosphodonor acetyl phosphate (McCleary and Stock, 1994; Wanner and Wilmes-Riesenberg, 1992; Wolfe, 2005). The cellular concentration of this high-energy source of phosphoryl groups is strongly dependent on growth conditions, such as carbon source (Wolfe, 2005). Concentrations can be quite high in some cases, which raises the possibility that acetyl phosphate may integrate metabolic status into some two-component systems (Fredericks et al., 2006; Klein et al., 2007; Wanner, 1992). For most two-component systems, however, a role for acetyl phosphate has not been established. There is also the possibility that other small molecule phosphodonors (Lukat et al., 1992) could function to phosphorylate response regulators in some contexts in vivo. The ability of a phosphodonor to affect the steady-state phosphorylation level of a particular response regulator depends on the rate of the phosphorylation reaction relative to the rates of other sources of phosphorylation and dephosphorylation. While phosphorylation rates of cytoplasmic fragments of histidine kinases are readily measured in vitro using purified components (Laub et al., 2007), the effects of individual phosphorylation sources on levels of phosphorylated response regulator in vivo can be quite sensitive to the strain background and can be difficult to measure. For example, a bifunctional histidine kinase that has high rates of phosphorylation and dephosphorylation can give levels of phosphorylated response regulator that are comparable to those that would arise from a histidine kinase that has weak phosphorylation activity but lacks a mechanism for response regulator dephosphorylation (Fig. 1.2A). However, through a careful and systematic analysis using transcriptional and translational reporter fusions, the contributions from different phosphorylation sources can in some cases be disentangled.
3. Cross-Talk Suppression Studies have shown that cross-talk can be suppressed by cognate histidine kinases and response regulators (Fig. 1.2B and C) (Groban et al., 2009; Kim et al., 1996; Silva et al., 1998; Siryaporn and Goulian, 2008).
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Figure 1.2 (A) Two different scenarios in which very different rates of phosphorylation can give the same steady state level of phosphorylated response regulator (RR-P). (Left) A bifunctional histidine kinase phosphorylates and dephosphorylates a response regulator with very high rates such that, under moderate stimulation, a moderate level of RR-P is obtained at steady state. (Right) A histidine kinase that phosphorylates a response regulator very weakly may, in the absence of any phosphatase activity, also produce moderate levels of RR-P. (B, C) Suppression of cross-talk from a histidine kinase HK2 to response regulator RR1 by cognate partners (Groban et al., 2009; Siryaporn and Goulian, 2008). (B) The high flux of phosphorylation and dephosphorylation of RR1 by the bifunctional histidine kinase HK1 can suppress or buffer against cross-talk from HK2. (C) Suppression of cross-talk by a cognate response regulator. The cognate response regulator RR2 outcompetes the noncognate regulator RR1 for interaction with the histidine kinase HK2. By removing RR2, significant cross-talk from HK2 to RR1 may be detected, which would be otherwise absent in the wild-type strain.
Mutations that relieve these suppression mechanisms could produce signals of cross-talk that would otherwise be squelched in the wild-type strain. To avoid over- or misinterpreting the results from analysis of mutant strains, it is important to take into account the potential effects of these mechanisms.
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Bifunctional histidine kinases set the level of phosphorylated response regulator through a balance of phosphorylation and dephosphorylation. This cycle can effectively act as a buffer to suppress the effects of weaker phosphorylation sources (Fig. 1.2B) (Groban et al., 2009; Kim et al., 1996; Silva et al., 1998; Siryaporn and Goulian, 2008). Therefore, deletion of a bifunctional histidine kinase could reveal phosphorylation of the response regulator from a noncognate histidine kinase or other phosphodonor (e.g., Batchelor et al., 2005; Danese and Silhavy, 1998; Hutchings et al., 2006; Wolfe et al., 2008). However, this alternate source may give a much lower flux of phosphoryl groups and may thus be physiologically irrelevant in wild-type strains. A response regulator can also prevent its cognate histidine kinase from participating in cross-talk (Fig. 1.2C). For example, cross-talk from CpxA to OmpR is suppressed by CpxR (Siryaporn and Goulian, 2008). This is likely to occur through competitive inhibition, in which the cognate response regulator outcompetes noncognate response regulators for interaction with the histidine kinase. Thus, deletion or inactivation of a response regulator could potentially produce cross-talk from the cognate histidine kinase to some other response regulator. Significant overexpression of the histidine kinase could also have a similar result.
4. Transcriptional Reporters In most two-component systems that have been studied, the phosphorylated response regulator controls transcription of effecter genes. Therefore, transcriptional reporter fusions have been a standard tool for measuring the output of signal transduction systems. A reporter gene such as gfp or lacZ is placed under control of a promoter of interest and expression of the reporter is measured through fluorescence or enzyme assays. Cross-talk can be measured using transcriptional reporter fusions. Chromosomal fusions are preferable to constructs on multicopy plasmids since they avoid potential effects on reporter gene expression from titrating out response regulator and changes in plasmid copy number. It is always a good idea to assess the dynamic range of reporter expression since this gives an indication of the sensitivity to changes in output under various conditions. The basal expression level can be determined from measurements in a strain deleted for the response regulator of interest. The range of activation can be determined by making measurements for different levels of input stimulus, if the stimulus is known, or by varying expression of a constitutively active version of the histidine kinase, if one is available. For experiments in which a fluorescent reporter such as gfp is used, it is preferable to grow cultures using a growth medium with a low level of autofluorescence such as a
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minimal salts medium (Miller, 1992), as this generally increases the sensitivity of measurements. Rich media often give moderate levels of autofluorescence, which could reduce the sensitivity of fluorescence measurements. Evidence for the presence and absence of cross-talk can be followed by monitoring changes in transcriptional reporter expression for different genetic backgrounds and conditions. Depending on the application, it may be necessary to remove cross-talk suppression mechanisms described above by deleting appropriate histidine kinases and response regulators before cross-talk can be detected. The observation of cross-talk in this case should not be used to conclude that there is cross-talk in the wild-type strain. Specific histidine kinases that are potential sources of cross-talk can be tested by changing expression levels or stimulation with signal. However, their effects on cross-talk will depend on details of the histidine kinase and its interaction with the noncognate response regulator, as discussed below. A histidine kinase sets the level of phosphorylated response regulator through phosphorylation alone (monofunctional behavior) or through a balance of phosphorylation and dephosphorylation (bifunctional behavior). For histidine kinases displaying monofunctional behavior, the level of phosphorylated response regulator is expected to be quite sensitive to histidine kinase expression level (E. Batchelor and M. Goulian, unpublished observations; Miyashiro and Goulian, 2008). On the other hand, when the histidine kinase has bifunctional behavior, modeling and experiments suggest the level of phosphorylated response regulator can be relatively insensitive to histidine kinase expression levels (Batchelor and Goulian, 2003; Miyashiro and Goulian, 2008; Shinar et al., 2007). Thus, cross-talk may be observed in a context of histidine kinase overexpression even though it appears that cognate response regulator phosphorylation is unchanged. In addition, in at least some cases, a histidine kinase can exhibit bifunctional behavior against its cognate partner while displaying monofunctional behavior against a noncognate partner (Siryaporn and Goulian, 2008). The choice of carbon source may have a significant effect on the level of acetyl phosphate in the cell (McCleary and Stock, 1994) and thus the level of response regulator phosphorylation. The desired level of acetyl phosphate may depend on the experimental condition being tested. In some cases, significant levels of acetyl phosphate may provide a means of increasing the basal level of response regulator phosphorylation and hence increase the sensitivity to cross-talk by other phosphodonors. In other cases, one may want to eliminate acetyl phosphate or keep its level to a minimum in order to distinguish its effects from other sources of response regulator phosphorylation. For Escherichia coli, growth using carbon sources such as glucose, pyruvate, and acetate produce high levels of acetyl phosphate. Low levels can be achieved by growing on glycerol. One can also modulate acetyl phosphate levels by deleting one or both of pta and ackA, depending on the flux of acetate through the Pta (phosphotransacetylase)–AckA (acetate
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kinase) pathway (Wolfe et al., 2008). Deletion of pta ackA abolishes production of acetyl phosphate. However, care should be taken in altering the Pta–AckA pathway as this may have additional indirect effects on some two-component systems (Wolfe et al., 2008). While transcriptional reporter fusions can provide effective measurements of two-component system output, they are also subject to the effects of other regulatory factors. There are many examples of genes whose expression is controlled by multiple regulatory proteins and small RNAs. Even many relatively well-characterized systems may have additional unidentified regulators whose effects could give the false impression of cross-talk. In some cases, promoters may also be controlled by multiple response regulators (e.g., Batchelor et al., 2005; Mouslim and Groisman, 2003). To distinguish between cross-talk and alternate regulatory pathways, one may need to use additional methods to follow response regulator phosphorylation or binding to promoters.
5. Response Regulator Localization If a functional fluorescent protein (FP) fusion to the response regulator of interest is available then one can use a fluorescence localization assay to provide further support for cross-talk in vivo (Batchelor and Goulian, 2006; Siryaporn and Goulian, 2008). The method takes advantage of the tendency of plasmids with partitioning systems to form clusters in the cell (Pogliano, 2002). In this assay, cells express the response regulator-FP fusion and also contain a plasmid that has one or more binding sites for the response regulator. Plasmid clustering provides a high local density of response regulator binding sites in the cell (Fig. 1.3A). Significant response regulator-FP binding to DNA results in intense localized regions of fluorescence, which appear as bright foci or spots under a fluorescence microscope and can be easily quantified by image analysis (Fig. 1.3B–D). Assuming DNA binding is modulated by response regulator phosphorylation, changes in response regulator-FP colocalization with the plasmid can be used to infer changes in response regulator phosphorylation. We have been able to construct functional fluorescent protein fusions to the C-termini of several different response regulators in E. coli, although in all of the cases that we tested the fusions showed decreased activity. We are unaware of any general rules regarding the choice of C- or N-termini for the fusion, or whether or not to include a flexible linker. It is probably best to try several different constructs. For the plasmid, we have used constructs based on pRS415 (Batchelor and Goulian, 2006; Simons et al., 1987), which is derived from pBR322. Many other plasmids should work as well although they may show differing levels of clustering. Plasmid clustering can
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Characterizing Cross-Talk In Vivo: Avoiding Pitfalls and Overinterpretation
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Figure 1.3 Measurement of response regulator binding to DNA in vivo. (A) Schematic of the method, which uses a functional translational fusion of a fluorescent protein (e.g., YFP) to the response regulator and a plasmid containing response regulator binding sites. The plasmid also contains lac operators, which are bound by CFP-LacI. Plasmid clustering in the cell results in a high local concentration of binding sites, which is easily visualized as a bright fluorescent spot in the CFP channel. Response regulator binding to plasmid also results in a bright fluorescent spot in the YFP channel. (B) Images of cells expressing OmpR-YFP and containing a plasmid with OmpR binding sites (top row) or an empty control vector (bottom row). DIC—differential interference contrast image. (C) Example of a YFP spot, corresponding to the dashed square in the upper right image in (B), and the associated pixel values and distances from the center. (D) Gaussian fit to the profile of pixel values from (C). A corresponding fit to the neighborhood of a maximal YFP pixel in a cell containing the vector control is also shown.
be characterized through the use of lac operators and a fluorescent protein fusion to lac repressor, as discussed below. The ability to detect fluorescent protein localization to plasmids will depend on the binding affinity of the
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phosphorylated response regulator-FP fusion as well as the extent of phosphorylation. One can increase the sensitivity by including several binding sites in the plasmid as needed. It is generally advisable to include a means to monitor plasmid clustering independently of response regulator binding to DNA. This controls for potential changes in plasmid clustering as a result of changes in cell physiology. This is easily accomplished by cloning lac operators into the plasmid and expressing a fluorescent protein fusion to lac repressor. We routinely use a CFP-LacI fusion (Batchelor and Goulian, 2006), which can be simultaneously imaged with a response regulator-YFP fusion. We have used an array of tandem lac operator repeats (Robinett et al., 1996) to mark the plasmid. This produces extremely bright foci, which is convenient as it provides increased sensitivity and enables shorter exposures when acquiring images. After plasmid clusters have been identified in the CFP channel, one can then quantify the YFP fluorescence in the neighborhood of this location. Under conditions of very high response regulator phosphorylation one may observe additional foci that are not associated with plasmids. In at least one case (OmpR-YFP), we have determined that these foci are due to response regulator binding to chromosomal loci (E. Libby and M. Goulian, manuscript in preparation). Fluorescence localization can be visualized using techniques adapted from general single-cell fluorescence microscopy methods (e.g., Batchelor and Goulian, 2006; Miyashiro and Goulian, 2007). 1. Grow cultures in medium with aeration at the appropriate growth temperature to saturation. To maximize the sensitivity of this assay, a culture medium that gives relatively low levels of autofluorescence should be used. A minimal salts medium is ideal in this respect and also allows for choice of carbon source. 2. Dilute cultures at least 1:1000 into the same medium and grow to early exponential phase (optical density of 0.1–0.3 at 600 nm for E. coli). 3. Shortly before cells reach the target density, preheat the microscope stage to the same temperature as that used for growing the cell cultures. Prepare 1% agarose pads made from the same growth medium and placed between a microscope slide and cover glass. For many smallscale experiments, it is sufficient to use 3 in. 1 in. 1 mm slides and 22 mm square #1.5 cover glass. Place 50–100 ml of molten agarose on the center of slide and cover immediately with coverslip. Agarose pads should be maintained at the growth temperature of the culture and may need time to dry slightly before use. Cells will not immobilize well on agarose pads that are too moist. 4. Carefully lift the cover glass off of the pad and place 5 ml of culture onto cover glass. Replace cover glass back onto agarose pad with culture between the cover glass and pad and put the slide on the
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microscope stage immediately. The number of cells on the agarose pad should be such that cells are easily found in most fields but are in sufficiently low density that there is little or no contact between neighboring cells. Obtaining proper cell density in images is especially important for the fluorescence analysis that follows. 5. Acquire a phase contrast or differential interference contrast (DIC) image and fluorescence images of the same field on a fluorescence microscope (Fig. 1.3B). Image acquisition times should be long enough so that cell fluorescence is significantly higher than background but brief enough so that none of the pixel values is equal to the maximum allowed value. The maximum pixel values are 255, 1023, 4095, and 65355 for 8-, 10- 12-, and 16-bit images, respectively. Objective magnification and resolution of the digital camera should be sufficient so that the shortest dimension of individual cells is at least approximately 5 pixels. Images should be saved without applying any contrast enhancements or other processing. Cell fluorescence levels may be relatively weak for response regulator fusions that are expressed at wild-type levels. In addition, fluorescent foci are not always observable through the eyepiece and may photo-bleach within seconds. It is important to acquire images from fields of cells that have not been previously exposed to light from the fluorescence illuminator. In particular, cells should be brought into focus using phase or DIC imaging. The extent of fluorescence localization will vary from cell to cell due to fluctuations in protein expression levels, plasmid copy number, plasmid localization, or other variations in the cellular environment. A representative measure of fluorescence localization for the population can be attained by acquiring images of a large number of cells (e.g., 100–200) and quantifying the extent of localization as described below. While dramatic changes in fluorescence localization may be sufficient to support a particular conclusion, in some cases it may be necessary to distinguish smaller changes in localization that are not easily assessed by eye. The fluorescence localization of cell populations can be quantified from images using software packages such as ImageJ (National Institutes of Health, Bethesda, MD), LabVIEW (National Instruments, Austin, TX), and MATLAB (The MathWorks, Natick, MA), which provide convenient libraries of imaging tools. Modules and plug-ins provided by these packages can be stitched together through scripts or macros to process large numbers of images quickly and accurately. The basic assumption behind this analysis is that fluorescent foci can be accurately represented by point-source intensity profiles. The maximum value of the fluorescence in the cell or the maximum value in the neighborhood of the plasmid cluster is taken to be the peak of the fluorescence intensity profile. Pixel values surrounding this maximum are extracted, assigned pixel distances, and fitted to a Gaussian function. Numerical
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parameters derived from the best-fit intensity profiles are then used to compute an integrated fluorescence intensity, which is a measure of the extent of fluorescence localization. This method assumes that YFP fluorescence and phase contrast or DIC images have been collected. Instructions also describe cases where the investigator has chosen to express CFP-LacI to identify plasmid clusters as described above. 1. A phase contrast or DIC image and a YFP fluorescence image should be available for analysis. If the CFP-LacI system was also used, then a CFP fluorescence image should also be on hand. 2. Determine cell boundaries using the phase or DIC image. Edge detection algorithms, such as the Sobel edge filter (Castleman, 1996), can be used for identifying cell boundaries in phase contrast images. For images which contain relatively low or complex contrast, such as images from DIC microscopy, cell boundaries can be identified by thresholding pixel values (Batchelor and Goulian, 2006; Miyashiro and Goulian, 2007). 3. Once cell boundaries are determined, it is often convenient to construct a binary (black and white) mask, which defines areas where cells are present. Masks can be constructed from cell boundary images by filling in regions marked by the cell boundaries. 4. Extract pixel values from corresponding fluorescence images using the mask as a guide for cell locations. It is convenient to extract pixel values for individual cells so that each cell can be processed individually from this point on. 5. Determine the location of the plasmid cluster within the cell as follows: (a) If a CFP-LacI image marking plasmid clusters has been acquired, the brightest pixel within each cell boundary in the CFP image should be identified. If the response regulator-YFP is colocalized with the plasmid cluster, it should be observable in the vicinity of the LacICFP peak. Identify the location of the maximum YFP peak within the local pixel neighborhood of the CFP peak. For example, for a cell that is represented by approximately 500 pixels, one might choose to restrict the search area to be within a 3 3 pixel area. This maximum YFP value is taken to be the peak of the YFP (response regulator) intensity profile. (b) If a CFP-LacI image is not available, identify the location of the maximum pixel value within the cell boundary of the YFP (response regulator) image. This is taken to be the peak of the YFP (response regulator) intensity profile. 6. Extract pixel values surrounding the peak of the YFP intensity profile (Fig. 1.3B). The extracted area should be large enough to encompass a section representative of the spot and should not include areas outside of the cell. Assign a radial distance to each pixel value with the peak pixel value as the center (Fig. 1.3C).
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7. Using a nonlinear fitting algorithm, such as the Levenberg-Marquardt algorithm (Press and Numerical Recipes Software (Firm), 1992), fit the pixel intensities I as a function of radial distance r to the Gaussian form I = A exp(Br2) þ C with fitting parameters A, B, and C (Fig. 1.3D). To compute the integrated peak fluorescence intensity, one can take the integral of the Gaussian using parameters for the best fit. One can also fit to a Gaussian that is not radially symmetric as described in Siryaporn and Goulian (2008). This has the advantage that it allows for asymmetric patterns of fluorescence in the neighborhood of fluorescence maxima near the boundary of the cell. The parameters describe intensity characteristics of the peak profile: A characterizes the peak intensity value, the inverse of B characterizes the width of the spot, and C characterizes the local background fluorescence. We have found the parameter A as well as the integral of the Gaussian over the fitting area can both provide reasonable measures of response regulatorYFP colocalization with plasmids. In some cases, the fits may give very poor representations of the fluorescence distribution profile. Obviously poor fits (e.g., negative parameters for A or C) can be thrown out using a predetermined cut-off criterion. When analyzing cell images, it is important to spotcheck parameters from individual cells and compare these to actual locations in the cell to verify that the program is working properly and that parameters accurately describe characteristics of the fluorescence profile.
6. Phosphatase Cross-Talk The effects of phosphatase cross-talk in principle should be readily detectable as a decrease in response regulator phosphorylation. However, the mechanisms described above will likely suppress weak phosphatase cross-talk in the same way that phosphorylation cross-talk is suppressed. To look for possible signals of phosphatase activity, it is therefore useful to establish a system for response regulator phosphorylation in the absence of the wild-type cognate histidine kinase. For some response regulators, this can be accomplished by growing cells under conditions of high acetyl phosphate (e.g., growth on glucose or pyruvate). Another approach is to use a mutant of the cognate histidine kinase that lacks phosphatase activity but retains kinase activity. Many of these kinaseþ phosphatase- mutants appear to function as relatively weak kinases. However, they nevertheless produce high levels of response regulator phosphorylation because of the absence of a mechanism for rapid response regulator dephosphorylation. If the histidine kinase being tested does show evidence of dephosphorylation activity against the noncognate response regulator, this is at least consistent with some level of phosphatase cross-talk. Of course all of the caveats
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described above regarding the relevance for wild-type cells still apply. In addition, one must consider the possibility that the decreased phosphorylation is due to an indirect effect, for example, from decreased response regulator expression, decreased expression of the (kinaseþ phosphatase-) mutant, or decreased acetyl phosphate levels.
7. Signal Response in Cross-Talk Networks Most histidine kinases that have been studied are associated with signal detection; they modulate the level of phosphorylated response regulator in response to changes in signal. In general, the signal could affect the rate of autophosphorylation or the rate of response regulator dephosphorylation. In some cases, modulation of the input signal for a specific histidine kinase could provide evidence for cross-talk to a noncognate response regulator. However, the interpretation of these results should be viewed with caution if other physiological effects of the input signal are not well understood. Indeed, the signals for many histidine kinases, such as osmolarity, unfolded proteins, pH, variations in specific ions, etc. are likely to have many effects on the cell through pathways distinct from the specific histidine kinase that one is attempting to stimulate. It is also quite possible that cross-talk is present but is blind to signal. Interestingly, this appears to be the case in some examples where cross-talk has been characterized (Silva et al., 1998; Siryaporn and Goulian, 2008). One model suggests that this is due to the relatively weak interactions between the noncognate histidine kinase and response regulator (Siryaporn et al., in preparation).
8. Concluding Remarks The level of phosphorylated response regulator is generally expected to be sensitive to perturbations in the rates of the phosphorylation and dephosphorylation reactions. Most wild-type circuits appear to be designed to protect against such sensitivity. However, in mutant backgrounds, in which cognate histidine kinases and response regulators have been deleted, sensitivity can emerge, resulting in cross-talk from noncognate histidine kinases or alternative phosphodonors. This can be useful for studying the specificity of histidine kinase–response regulator interactions, but does not necessarily indicate a physiological role of cross-talk, or even the existence of cross-talk, in wild-type cells. Indirect effects on histidine kinase or response regulator expression level, the level of other phosphodonors, or possibly other regulators can also give an easily misinterpreted signal of cross-talk in mutant backgrounds. Nevertheless, through a careful and
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systematic dissection of two-component networks, the level of insulation, and flow of phosphorylation between individual components can be disentangled from the complex web of interacting regulatory pathways.
ACKNOWLEDGMENTS The work was supported by NIH grant R01GM080279 to M. G. A. S. was also supported by the NIH Bacteriology Training Grant T32 AI060516.
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