INTRODUCTION

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Green fluorescent protein (GFP) has become a popular reporter for gene activity in bacteria. In this capacity, it is generally being used in one of two ways: either to establish the conditional expression of a gene in response to a specific substance, growth condition, or habitat (5, 6, 15, 21, 23, 40, 45, 48) or to analyze a sample or habitat for a substance or condition to which a particular gene is known to be responsive (3, 8, 10, 16, 17, 22, 26, 35). In both cases, the assessment of whether or not a particular gene or its promoter is responsive is based on the comparison of GFP fluorescence in cells from two populations: for example, one grown in the presence of a substance and the other grown in its absence or one grown in vivo compared to the other in vitro.

The experimental set-up usually dictates the method by which GFP fluorescence is quantified. Cell suspensions or culture aliquots are commonly analyzed by fluorimetry. Fluorescence measured this way is normalized for the number of cells in the sample to obtain an average fluorescence per cell, which can then be compared to that of other cell suspensions. Using fluorescent flow cytometry or image cytometry, which are more sensitive methods, it is possible to measure GFP fluorescence emitted from individual bacteria. Such approaches become necessary when cells have to be analyzed directly in situ, e.g., to establish habitat-specific gene expression (46), when cells are so few that their combined fluorescence drops below the detection capacity of a fluorimeter, or when it is anticipated that the bacterial population under investigation is divided into subpopulations that are exposed to different conditions (26). Single-cell GFP contents are often represented in histograms or normal probability plots, which offer the advantage of instant appreciation for the variation among cells within the same population and present a convenient way of comparing GFP content in cells from different populations.

Ideally, the output of a reporter protein should reflect as closely as possible the activity of the promoter that drives its expression. GFP fluorescence has been validated as a reporter output by direct comparison to more traditional reporter proteins such as -galactosidase (28, 39) and chloramphenicol acetyltransferase (1). Still, two properties that are unique to GFP have been recognized as less desirable when trying to infer promoter activity from fluorescence measurements. First, newly synthesized GFP must undergo a series of self-modifications in order to become fluorescent (44). The rate-limiting step in this maturation process requires oxygen and occurs with pseudo-first-order kinetics (18), which means for the wild-type GFP from the jellyfish Aequorea victoria (11) that the appearance of fluorescence lags some 3.5 h behind the actual synthesis of the protein (2). Second, GFP is unusually resistant to proteolysis (44), with a half-life time reported as >1 day in vivo (4). This means that once made, GFP will persist in a cell even after the promoter that drives its expression is shut down. Both these properties have been addressed by the creation of GFP variants such as enhanced GFP (EGFP) (34) and GFPmut3 (12), which have significantly reduced maturation times, and unstable variants GFP[ASV], GFP[AAV], and GFP[LVA] (4), which were engineered to become susceptible to degradation by ClpXP-type proteases (14, 24).

With the need for GFP variants such as those described above comes the realization that promoter activity is not the only factor governing a cell's GFP content. Conversely, a shift in GFP content is not necessarily indicative of a change in promoter activity. For example, a decrease in protease activity may well cause an accumulation of GFP in bacterial reporter cells, which in turn could easily be misinterpreted as an increase in promoter activity. Another important factor to consider is growth rate: the faster cells divide, the faster GFP is diluted. Therefore, growth rate should be expected to have a considerable impact on GFP content. It was mentioned, for example, that Pseudoalteromonas cells expressing GFP appeared dimmer on rich medium than on minimal medium (42).

We set out to understand how exactly promoter activity, maturation rate, proteolytic degradation, and cell division rate in combination determine GFP content. Our approach was based on the formulation of a structured numerical model that describes the accumulation of fluorescent GFP from a promoter-gfp fusion in a single bacterial cell. The model proved to be very useful by providing us with a set of formulas that made it possible to extract parameter values from actual fluorescence measurements. These parameters then allowed us to accurately predict and interpret the accumulation of fluorescent GFP, both in bacterial cultures and in individual bacterial cells.

	MATERIALS AND METHODS

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Bacterial strains, culture conditions, and promoter-gfp constructs. Escherichia coli DH5 (38) and Erwinia herbicola 299R (9) were grown aerobically in Luria-Bertani (LB) broth or M9 minimal medium (38) supplemented with 0.2% Casamino Acids (Difco Laboratories, Detroit, Mich.) plus 0.4% galactose or fructose. Where appropriate, kanamycin (Km) and tetracycline (Tc) were added at final concentrations of 50 and 15 µg/ml, respectively.

Determination of culture growth and GFP fluorescence in bacterial cultures and in individual cells. Bacterial growth was followed as optical density (OD) at 600 nm using a Perkin-Elmer Lambda 3A UV/VIS spectrophotometer (Perkin-Elmer, Norwalk, Conn.). GFP fluorescence in bacterial cultures was determined in a Perkin-Elmer LS50B luminescence spectrometer that was set at an excitation wavelength of 490 nm and an emission wavelength of 510 nm (with a slit width of 8 nm in both cases). GFP content of individual cells was determined as described previously (26) by epifluorescence microscopy on a Zeiss Axiophot microscope (Zeiss, Oberkochen, Germany) and quantitative image analysis using IPLab software (Scanalytics, Fairfax, Va.).

Simulations and best-fit procedures. All simulations of the model were performed with Gepasi software version 3.21 (30) or in Microsoft Excel (Microsoft Corporation, Redmond, Wash.). The response of the P_A1/O4/O3 promoter to different concentrations of IPTG was fitted to the Hill equation using GraphPad Prism version 3.00 for Windows (GraphPad Software, San Diego, Calif.).

	RESULTS

Top Abstract Introduction Materials and Methods Results Discussion References

Formulation of a model for GFP accumulation in single bacterial cells. We formulated a structured model that is similar to but differs in important ways from the one described by Subramanian and Srienc for GFP accumulation in transfected mammalian cells (43). Consider a bacterial cell that harbors a gene for GFP fused to promoter P. Its fluorescent GFP content (^fGFP) will depend on the rate at which nonfluorescent GFP (ⁿGFP) is synthesized from P, on the rate of maturation from ⁿGFP to ^fGFP, on the growth rate of the bacterium, and on the rate with which both ⁿGFP and ^fGFP are degraded by proteases (Fig. 1). Changes in ⁿGFP and ^fGFP over time can be expressed as follows:

(1)

and

(2)

in which n is ⁿGFP content (ⁿGFP per cell), t is time (hours), P is promoter activity (ⁿGFP per cell per hour), m is the maturation constant (per hour), µ is the growth rate (per hour), D_n is the degradation rate of ⁿGFP (ⁿGFP per cell per hour), f is ^fGFP content (^fGFP per cell), and D_f is the degradation rate of ^fGFP (^fGFP per cell per hour). Whereas Subramanian and Srienc dissected the output from promoter P into single-component parameters such as transcription initiation rate, mRNA stability, and translation efficiency, we combined the kinetics of transcription and translation into the single value P. 

View larger version (11K): [in this window] [in a new window]   FIG. 1.   Flow diagram showing how a bacterium's fluorescent GFP content (f) is a function of maturation from a pool of nonfluorescent GFP (n), degradation by proteases, and dilution by cell division. The pool of n is depleted by maturation, degradation, and dilution and replenished by transcription and translation from a promoter-gfp fusion.

(3)

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Adoption of single-cell model to describe GFP accumulation in bacterial cultures. With the model we describe above, it is possible to simulate the dynamics of fluorescent GFP expression in a single bacterial cell with the help of a spreadsheet program like Microsoft Excel or a more sophisticated application like Gepasi (30). In these simulations, parameters such as promoter strength, degradation capacity, and growth rate can be changed freely to assess how they affect, individually or in combination, the rates and levels of GFP accumulation. In this and the following sections, we will describe how parameter values for P, D_max, and K_M can be approximated from fluorescence measurements on growing bacterial cultures, and go on to show how these values can be used to accurately predict patterns of GFP accumulation in bacterial individuals or populations.

(9)

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Estimation of promoter activities from experimental GFP fluorescence data using F,OD plots. Equations 5 and 6 can be combined as:

(12)

If GFP is stable and not subject to proteolytic degradation, D_nss and D_fss equal zero, so that equation 12 can be simplified to:

(13)

Note how a reduction in µ would cause an increase in f_ss given a constant value for P. In other words, a cell expressing GFP from a promoter with constant activity will appear brighter if it is growing more slowly. If not corrected for this growth effect, the promoter activity in this cell would be overestimated based on GFP fluorescence alone.

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View larger version (8K): [in this window] [in a new window]   FIG. 2.   Accumulation of GFP fluorescence in cultures of E. herbicola 299R::PA1/O4/O3-gfpmut3(pCPP39) growing on galactose in the presence (solid squares) or absence (open squares) of IPTG. Fluorescence was normalized for cell density and plotted as a function of time (A). Broken horizontal lines indicate steady-state levels of fluorescent GFP that were predicted from the slopes in a corresponding F,OD plot (B). Growth was exponential in both cultures and occurred at the same fast rate (C; open squares largely overlap solid squares).

IPTG

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IPTG

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IPTG

IPTG

Application of the model: quantitative description of the P_A1/O4/O3 promoter. We grew cultures of E. herbicola 299R::P_A1/O4/O3-gfpmut3(pCPP39) in the presence of increasing amounts of IPTG and determined in each case a corresponding value for P, as described above. The resulting curve had a sigmoidal shape that is typical of a saturable response (Fig. 3). The range of IPTG concentrations over which P_A1/O4/O3 activity could be modulated was quite narrow, between 0.1 and 1 mM. We fitted the observed data points to the Hill equation (20):

(14)

in which P_max is the maximal promoter activity (RNU per OD unit per hour), [IPTG] is the concentration of IPTG in the culture medium (micromolar), h is the Hill coefficient (unitless), and K is the IPTG concentration at which P equals 1/2 · P_max. The fit (Fig. 3, bold line) revealed that P_max equaled 720 ± 13 RNU OD¹ h¹ and K = 323 ± 15 µM. We measured P_[IPTG]=0 = 1 ± 1 RNU OD¹ h¹, which means that P_A1/O4/O3 activity was adjustable over a >720-fold range.

View larger version (10K): [in this window] [in a new window]   FIG. 3.   Modulation of PA1/O4/O3 activity by IPTG. Cultures of E. herbicola 299R::PA1/O4/O3-gfpmut3(pCPP39) were grown in LB broth supplemented with different concentrations of IPTG (molar). The corresponding promoter activities were plotted as a function of log[IPTG]. The bold line shows the best fit through the data points using the Hill equation (see text for parameters). The thin line represents the same function but with a Hill coefficient of 1.

Accumulation of fluorescence in cultures expressing unstable variants of GFP. To investigate the effect of GFP stability on the accumulation of fluorescence, we repeated the IPTG induction experiments with cells of E. herbicola 299R::P_A1/O4/O3-gfp(pCPP39) that had the gene for stable GFP replaced with that for one of the unstable variants GFP[AAV], GFP[ASV], or GFP[LVA] (4). From the resulting F,OD plots (not shown), we obtained estimates for steady-state fluorescence f_ss and calculated corresponding values for P using equation 13. Under the assumption that degradation of GFP obeys Michaelis-Menten kinetics, this apparent P, or P_app, is equal to P  D_nss  D_fss · (1 + µ/m) and quantifies how much of promoter activity P actually goes into making fluorescent GFP. The rest, i.e., P  P_app, or D_nss + D_fss · (1 + µ/m), is wasted on proteolytic degradation. This "waste" can also be written as D_nss + D_fss + D_fss · µ/m, or D_max · (n_ss + f_ss + µ · f_ss/m)/(n_ss + f_ss + K_M). At high values of P, when GFP content is much larger than the Michaelis-Menten constant (n_ss + f_ss K_M), this approximates to D_max · (n_ss + f_ss + µ · f_ss/m)/(n_ss + f_ss), or D_max · (1 + a · µ/m), in which a is the fraction f_ss/(n_ss + f_ss). As a must lie between 0 and 1, P  P_app amounts to anywhere between D_max (if a = 0) and (1 + µ/m) · D_max (if a = 1), and because a tends to change relatively little in the range of high promoter activities, P  P_app is essentially constant. If, on the other hand, the degradation of GFP is a first-order reaction with constant k, then P_app would be equal to P · b/c · (b + m)/(c + m), in which b = µ and c = µ + k. The waste in this case would be equal to P  P_app = P · [1  b/c · (b + m)/(c + m)], which is not constant but proportional to P.

View larger version (11K): [in this window] [in a new window]   FIG. 4.   Comparison of apparent and true promoter activities (Papp and P, respectively) as a test for the degradation kinetics of unstable variants GFP[AAV] (A), GFP[LVA] (B), and GFP[ASV] (C). Cultures of E. herbicola 299R::PA1/O4/O3-gfp(pCPP39) expressing GFP[AAV], GFP[LVA], or GFP[ASV] were grown in LB in the presence of 80, 160, 320, 640, or 1,280 µM IPTG. From F,OD plots, we determined values for F/OD and calculated corresponding apparent values for P from equation 13. These values of Papp were plotted as a function of the corresponding values for true promoter activity P, which were determined from a culture of 299R::PA1/O4/O3-gfp(pCPP39) expressing stable GFP in response to the same concentrations of IPTG. The dashed line represents the reference line Papp = P, whereas the solid lines represent a Gepasi simulation of Papp as a function of P, assuming experimentally determined estimates for Dmax and KM for each of the unstable GFP variants (see Fig. 5 and text).

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Estimation of parameters that determine the proteolytic degradation of unstable GFP variants. The F,OD plot of a culture expressing unstable GFP from a promoter with known activity P can be used to derive steady-state values for D_n and D_f, which can then be used to estimate D_max and K_M for the unstable variant, as follows. First, slope F/OD supplies the value for f_ss, which is used to calculate n_ss using equation 7. Next, D_nss and D_fss are obtained from equations 5 and 6. By combination of equations 3 and 4, it follows that during steady state:

(15)

which is essentially a Michaelis-Menten equation that describes the sum of D_nss and D_fss as a function of the sum of n_ss and f_ss. This equation can be transformed into a Lineweaver-Burk equation:

(16)

A plot of 1/(D_nss + D_fss) versus 1/(n_ss + f_ss) should yield a line with a slope of K_M/D_max and a vertical intercept of 1/D_max. From the slope and intercept, values for D_max and K_M can be computed. However, it should be noted that a single value of P yields only one value of n_ss, f_ss, D_nss, and D_fss, i.e., only one value for 1/(D_nss + D_fss) and one for 1/(n_ss + f_ss), which together produce a single point, not a line, on the Lineweaver-Burk plot. To obtain more points, promoter activity P will have to be varied to produce for each value P a set of cognate values for n_ss, f_ss, D_nss, and D_fss.

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View larger version (11K): [in this window] [in a new window]   FIG. 5.   Lineweaver-Burk plot to extract values for Dmax and KM that determine GFP[AAV] degradation in E. herbicola 299R. Cultures of 299R::PA1/O4/O3-gfp[AAV](pCPP39) were grown in LB in the presence of 80, 160, or 320 µM IPTG. From F,OD plots, we determined F/OD values of 1.7, 7.71, and 128 RLU OD1 h1, respectively. These were substituted for fss in equation 8, together with the respective values for P from Fig. 3, to calculate nss. From equations 5 and 6, we then calculated cognate values for Dnss and Dfss, substituting µ with observed values of 0.69, 0.69, and 0.73 h1, respectively. The inverse of the sum of nss and fss was plotted as a function of the inverse of (Dnss + Dfss) to produce this graph. From the best fit through the data points, Dmax was calculated as 232 RNU (or RLU) OD1 h1, and KM as 35 RNU (or RLU) OD1. The Dmax and KM parameters for GFP[ASV] and GFP[LVA] were determined the same way using strains 299R::PA1/O4/O3-gfp[ASV](pCPP39) and 299R::PA1/O4/O3-gfp[LVA](pCPP39), respectively.

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View larger version (9K): [in this window] [in a new window]   FIG. 6.   Combined effect of promoter strength and GFP stability on the accumulation of fluorescence in E. herbicola 299R. (A) Using Gepasi, we simulated steady-state levels of fluorescent GFP for a range of promoter strengths and determined the corresponding apparent promoter activities (see text for details). For the simulations, we assumed experimentally observed values for growth rate µ of 0.65 to 0.67 h1, and for m we used a value of 1.54 h1. Shown is a plot of Papp/P as a function of P. (B) Cell density-normalized fluorescence in cultures of 299R carrying pPfruB-gfp[tagless], -gfp[ASV], -gfp[AAV], or -gfp[LVA] grown on fructose (solid squares) or galactose (open squares). Galactose-grown cells in mid-exponential phase were transferred to fresh medium with fructose or galactose at t = 0 h.

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View larger version (9K): [in this window] [in a new window]   FIG. 7.   Combined effect of promoter strength and GFP stability on the accumulation of fluorescence in E. coli DH5. (A) Papp/P versus P plot for DH5. Simulations were done as described for Fig. 5. Values for Dmax and KM used were 1,781 RNU (or RLU) OD1 h1 and 9,272 RNU (or RLU) OD1 for GFP[ASV], 699 and 262 for GFP[AAV], and 1,311 and 390 for GFP[LVA], respectively. These values were obtained as described for Fig. 5, except that instead of the IPTG-responsive PA1/04/03 promoter, we used the fructose-responsive PfruB promoter to drive expression of unstable GFPs. For the simulations, we assumed experimentally observed values for growth rate µ of 0.35 to 0.46 h1, and for m we used a value of 1.54 h1. (B) Cell density-normalized fluorescence in cultures of DH5 carrying pPfruB-gfp[tagless], -gfp[ASV], -gfp[AAV], or -gfp[LVA] grown on fructose (solid squares) or galactose (open squares). Overnight LB-grown cells were transferred to fresh M9 medium with fructose or galactose at t = 0 h.

GFP accumulation in single cells within a bacterial population. Many applications of GFP as a reporter of promoter activity are concerned with GFP expression at the level of single cells rather than bacterial cultures. We have previously examined the GFP content of individual cells from fructose-induced and uninduced cultures of E. herbicola 299R carrying a P_fruB-gfp[AAV] fusion (26). Single-cell GFP content in cultures exposed to fructose appeared to be almost normally distributed (Fig. 8A). We could easily simulate this distribution assuming that promoter activity is not exactly the same for each cell in the population but instead normally distributed around an average value (Fig. 8B). The source of this variability in promoter activity may be intrinsic to the promoter itself or may be attributed to variation in copy number of the plasmid carrying the P_fruB-gfp[AAV] fusion.

View larger version (21K): [in this window] [in a new window]   FIG. 8.   Actual and simulated distributions of single-cell GFP content in fructose-induced (A and B) and uninduced (C and D) cells of E. herbicola 299R carrying plasmid pPfruB-gfp[AAV]. In the normal probability plots shown here, GFP fluorescence of individual cells is expressed on the horizontal axis as the fraction of the population's average single-cell GFP content. In this representation, normally distributed GFP content would yield a straight line. Panels A and C represent the results from actual induction experiments published elsewhere (26). The insets in both of these panels show the corresponding data in histogram format, with GFP content expressed in units of mean pixel intensity. For the simulation of fructose-induced GFP accumulation (B), we determined by using the model the steady-state GFP content (fss) for a collection of 300 individual cells, assuming that the promoter activity varied between cells around an average value of 1,160 RNU OD1 h1 with a standard deviation equal to one-third of the average, i.e, 387 RNU OD1 h1 (see inset). For the uninduced simulation (D), we assumed the same proportional variation (i.e., one-third) in promoter activity, i.e., P = 168 ± 56 RNU OD1 h1. In both simulations, we assumed µ = 0.67 h1, and for GFP[AAV] Dmax = 232 RNU (or RLU) OD1 h1 and KM = 35 RNU (or RLU) OD1.

Effect of growth rate on accumulation of fluorescent GFP. We have mentioned several times already that growth rate is an important determinant of a cell's GFP content. We performed a series of simulations that demonstrate this. If we assume that promoter activity is independent of growth rate µ (Fig. 9A, line marked 1), steady-state fluorescent GFP content becomes inversely proportional to µ (Fig. 9B, curve marked 1). The shape of the curve suggests that when cells are growing slowly, small changes in µ will have a much greater effect on GFP fluorescence than when cells are growing faster. It is probably unjustified to assume that P is independent of µ: it has been established that the activity of many promoters is in fact a function of growth rate (27). But even when we assume a more realistic dependency of P on µ, such as that for the constitutive promoter P_spc in E. coli (from reference 27) (Fig. 9A, line marked 2), GFP content remains inversely proportional to growth rate (Fig. 9B, curve marked 2). The same effect has been observed in E. coli using -galactosidase as a reporter protein (27).

View larger version (14K): [in this window] [in a new window]   FIG. 9.   Effect of growth rate on accumulation of GFP fluorescence. (A) We assumed three different dependencies of promoter activity P on growth rate µ: 1, none; 2, as for constitutive promoter Pspc (27); and 3, as for growth rate-responsive promoter Prrn (27). Data points for 2 and 3 were estimated from reference 27, with the following modification: the promoter activity at a given growth rate was expressed as the fraction of the maximal attainable promoter activity. (B) With the help of the model, steady-state GFP content fss was simulated as a function of growth rate µ, assuming the interdependencies given in panel A. As a reference point, we arbitrarily chose a growth rate of µ = 0.78 h1 and a promoter activity P = 360 RNU OD1 h1, which corresponds to E. herbicola 299R::PA1/O4/O3-gfpmut3(pCPP39) cells exposed to 323 µM IPTG (Fig. 3). The thin line represents a simulation of fluorescent GFP accumulation from a Prrn-gfp[AAV] fusion.

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	DISCUSSION

Top Abstract Introduction Materials and Methods Results Discussion References

We have presented a theoretical model for GFP accumulation in bacterial cells that is in good agreement with experimental observations. The model can be adopted in its present state to describe, predict, and interpret fluorescence data from any GFP-based reporter system other than the ones described in this paper. In fact, with the proper modifications, it can be put to use right away to model the expression of other reporter proteins as well.

Our model comes with several unique practical properties. First, we derived a set of formulas and tools from the model that allow easy extraction of parameter values from experimental data. For example, an estimate for promoter activity P can be readily obtained from a simple F,OD plot in combination with equation 13. Similarly, values for D_max and K_M of an unstable GFP variant can be estimated from a limited number of data points. Such a hands-on approach should be generally applicable to obtain similar quantitative information for any other promoter-gfp fusion. And once such information is available, simulation of GFP expression using a program like Gepasi is no longer an abstract exercise, but can be related directly to experimental observations. For those who are interested, a copy of the Gepasi file that was used for our GFP simulations will be made available upon request. This practical orientation of our model circumvents the need for more complicated means of data fitting that are often less accessible to those who have no or little familiarity with modeling practices. For example, it would be possible to extract values for P from the plot of F/OD as a function of time t in Fig. 2A by nonlinear curve fitting, but this is no trivial task. We should acknowledge, however, that we were able to implement our approach because the model is relatively simple, i.e., there are a limited number of parameters and variables involved.

Unstable GFP variants such as GFP[AAV], GFP[ASV], and GFP[LVA] have been used with great success as reporters of transiently expressed genes (3, 26, 35, 41). When they were originally reported (4), the unstability of these GFPs was described in terms of half-life times. This does not justify their performance in bacterial cells, as we have demonstrated here, both theoretically and experimentally. Instead of first-order kinetics, the degradation of these variants appears to follow Michaelis-Menten kinetics, and GFP fluorescence needs to be interpreted accordingly. In combination with an inducible promoter, an observed increase in GFP content actually overestimates the real increase in promoter activity, as Fig. 6A and 7A suggest. Depending on the GFP variant that is being used and on the bacterial host that harbors the fusion, that overestimation might become large enough to critically misconstrue GFP fluorescence data. The number of fusions constructed to date that harbor unstable GFPs as reporters of gene expression is still limited, so it will be interesting to see if our hypothesis will hold up as more reports on the use of these variants come out.

There is another good reason for caution with the use of unstable GFPs in reporter fusions. It was recently shown that the activity of the proteases that are thought to be responsible for the degradation of unstable variants like GFP[AAV] is modulated by an auxiliary protein whose expression appears to vary with growth rate (25). This suggests that the parameter D_max which describes proteolytic activity might be intimately correlated with growth rate. We believe that the increasing popularity of these unstable GFP variants in bacterial reporters (3, 26, 35, 41) invites a closer look into exactly how variations in protease activity quantitatively influence the translation of GFP signal into promoter activity. Certainly, the model described herein provides a valuable framework to start exploring and addressing these questions.

An important lesson from the model is that GFP fluorescence is a function of more than just promoter activity. One important determinant is growth rate, as the simulations in Fig. 9A made clear. We pointed out that small changes in low growth rates have a proportionally large impact on GFP content. This becomes especially relevant, for example, when bacterial bioreporters are employed in complex environments such as soil, where bacterial growth is generally slow and different from one spot to the next. Microlocalities that permit slightly faster growth will contain significantly dimmer bacteria. This effect of growth on GFP content might partially or entirely mask an increase in promoter activity, so that the reporter signal will be effectively underestimated. On the other hand, cells growing slightly slower will have significantly increased GFP levels that might be interpreted falsely as gene induction.

Unfortunately, an accurate determination of growth rate is not always possible. For cells growing in a culture flask, growth rate can be estimated quite easily by monitoring the population increase over time. Many GFP-based bacterial reporter cells, however, are used in an environmental setting that does not allow this approach, e.g., due to substantial heterogeneity in growth rate within the population. Fluorescent in situ hybridization offers a means to quantify ribosome content in individual cells and can provide a reasonable rough estimate of growth rate (37, 47). Such an approach was successfully employed to interpret GFP data from bacterial bioreporters of sugars on plant leaf surfaces (26). Another approach to assess the effect of growth rate may be to relate the output of a bioreporter strain to the inversely proportional fluorescent signal of a control bioreporter that expresses GFP from a constitutive promoter.

The model and its predictions have already proven very useful in our present work. One way it has been informative is in the approximation of plasmid copy number. For example, by comparison of promoter activity in cells that carry plasmid pP_fruB-gfp with that in cells carrying the same fusion in monocopy on the chromosome (not shown), we were able to estimate that the plasmid is present in 10 to 30 copies per cell in E. herbicola 299R. We routinely turn to Fig. 6 and 7 to decide which variant of GFP to choose in combination with a particular promoter. For example, Fig. 6 cautions against the use of gfp[LVA] in combination with a promoter activity of less than 400 RNU OD¹ h¹ in 299R because the cells would not be fluorescent. In bacterial strains that do not tolerate very high levels of GFP, like 299R, we commonly use strong promoters in combination with GFP[ASV] instead of stable GFP in order to somewhat reduce GFP content.

We regularly exploit the disproportional degradation of GFP[AAV] and GFP[LVA] in E. herbicola 299R to maximize apparent induction levels, as was shown for the P_fruB promoter (Fig. 6). Conceptually, this should work for every promoter with an already high level of expression when uninduced. By artificially decreasing this basal level using unstable variants, the interpretation of GFP fluorescence as an indicator for increased promoter activity will be facilitated considerably. However, as Fig. 8 shows, the use of unstable GFP variants may come at a price at the level of individual cells. At low promoter activities, GFP content is no longer normally distributed among cells, so that it becomes harder to translate a single cell's GFP content back into a corresponding promoter activity. Depending on the promoter that is being used, this may or may not be a problem. With promoters of the on/off type, the difference between the brightest uninduced cell and the dimmest induced cell may be large enough to state unambiguously whether or not a particular reporter cell is exposed to a stimulus. Other promoters are more graded in their response, and in that case, nonnormal overlapping frequency distributions will make it very difficult to assign a promoter activity to the observed fluorescent state of a single bacterium. Possibly, the model will be able to serve an interpretive function in the analysis of such complex GFP data.