Volume 13 Supplement 4
Characterization of an inducible promoter in different DNA copy number conditions
© Zucca et al.; licensee BioMed Central Ltd. 2012
Published: 28 March 2012
The bottom-up programming of living organisms to implement novel user-defined biological capabilities is one of the main goals of synthetic biology. Currently, a predominant problem connected with the construction of even simple synthetic biological systems is the unpredictability of the genetic circuitry when assembled and incorporated in living cells. Copy number, transcriptional/translational demand and toxicity of the DNA-encoded functions are some of the major factors which may lead to cell overburdening and thus to nonlinear effects on system output. It is important to disclose the linearity working boundaries of engineered biological systems when dealing with such phenomena.
The output of an N-3-oxohexanoyl-L-homoserine lactone (HSL)-inducible RFP-expressing device was studied in Escherichia coli in different copy number contexts, ranging from 1 copy per cell (integrated in the genome) to hundreds (via multicopy plasmids). The system is composed by a luxR constitutive expression cassette and a RFP gene regulated by the luxI promoter, which is activated by the HSL-LuxR complex. System output, in terms of promoter activity as a function of HSL concentration, was assessed relative to the one of a reference promoter in identical conditions by using the Relative Promoter Units (RPU) approach. Nonlinear effects were observed in the maximum activity, which is identical in single and low copy conditions, while it decreases for higher copy number conditions. In order to properly compare the luxI promoter strength among all the conditions, a mathematical modeling approach was used to relate the promoter activity to the estimated HSL-LuxR complex concentration, which is the actual activator of transcription. During model fitting, a correlation between the copy number and the dissociation constant of HSL-LuxR complex and luxI promoter was observed.
Even in a simple inducible system, nonlinear effects are observed and non-trivial data processing is necessary to fully characterize its operation. The in-depth analysis of model systems like this can contribute to the advances in the synthetic biology field, since increasing the knowledge about linearity and working boundaries of biological phenomena could lead to a more rational design of artificial systems, also through mathematical models, which, for example, have been used here to study hard-to-predict interactions.
Synthetic biology aims at implementing novel and user-defined capabilities in living systems that could yield applications of remarkable importance, like bioremediation or production of renewable fuels, new biomaterials and therapeutic molecules [1–3]. Engineering plays a crucial role in the rational design and construction of such systems, as principles like standardization, modularity and predictability of biological parts are considered key aspects . The synthetic biology paradigm can be summarized as follows: i) choose biological parts from a library of well-characterized standard DNA components; ii) assemble them together to obtain a genetic program that encodes the desired function; iii) incorporate it in a living organism to ultimate the job, as it is carried out in many fields of engineering. Although the development of DNA standards (such as the BioBrick™in the Registry of Standard Biological Parts) [5, 6] and automated DNA assembly platforms [7, 8] have significantly reduced the complexity of the genetic program composition process, each of the mentioned steps hides noteworthy difficulties [9, 10]: inability to provide reproducible quantitative characterization of parts , crosstalk or incompatibility among components , time-consuming debugging of systems , variability connected with biological processes  and, finally, a high failure rate of the engineered organism due to DNA mutation . Taken together, these points contribute to the inability to predict the behaviour of even simple synthetic biological systems. In fact, although relatively complex systems such as bistable switches , oscillators [16, 17] logic functions [18–22], amplifiers  have been successfully built up in bacteria, yeasts or mammalian cells and a variety of computational tools have been developed to aid their design [24–26], the construction of predictable biological systems from the bottom-up is currently a main challenge [9, 27]. Trial-and-error or directed evolution screenings are still commonly used to optimize and even repair suboptimal genetic circuits [18, 28–30]. Incorporation of genetic programs in a host, such as a bacterial cell, can be performed either through plasmid vectors, which are able to autonomously replicate in the organism and propagate the DNA-encoded synthetic functions to the progeny [31, 32], or through genomic integration in which the program is stably kept in the cell in single copy . Plasmids are maintained in cells at a copy number ranging from 1 or 2 to hundreds of copies, depending on their replication origin .
Strains and plasmids
Strains, plasmids and biological devices used in this work.
F- gyrA462 endA1 glnV44 Δ(sr1-recA) mcrB mrr hsdS20 ara14 galK2 lacY1 proA2 rpsL20(Sm r ) xyl5 Δleu mtl1
F-, Δ(argF-lac)169, ΔuidA4::pir-116, recA1, rpoS396(Am), endA9(del-ins)::FRT, rph-1, hsdR514, rob-1, creC510
CGSC, Yale University, USA
F- mcrA Δ(mrr-hsdRMS-mcrBC) ϕ 80lacZΔM15 ΔlacX74 nupG recA1 araD139 Δ(ara-leu)7697 galE15 galK16 rpsL(Str R ) endA1 λ-
F- λ-ilvG- rfb-50 rph-1
CGSC Yale University, USA
MG1655, Φ80(HSL RFP )
MG1655, Φ80(101 RFP )
MG1655, Φ80(IQ RFP )
Vector backbones used for RFP expression or genomic integration of the devices
pUC19-derived pMB1 origin (high copy)
pMR101-derived p15A origin (medium copy)
pSC101 origin (low copy)
R6K conditional origin, used for integration
HSL-inducible mRFP1 expression system
HSL-inducible GFPmut3b expression system
Standard reference constitutive promoter with mRFP1 expression device downstream
Standard reference constitutive promoter with GFPmut3b expression device downstream
lacIQ constitutive promoter with mRFP1 expression device downstream
Chemically competent TOP10 and DB3.1 were used according to manufacturer's instructions. Chemically competent MG1655 and BW23474 were prepared according to  and were transformed by heat shock at 42°C. All the strains were routinely grown in a 5-ml volume of LB medium  at 37°C and 220 rpm. When required, Ampicillin (100 mg/L), Kanamycin (20 mg/L) or Chloramphenicol (12.5 mg/L) were added to cultures to maintain plasmids. Long-term glycerol stocks, routinely stored at -80°C, were prepared for all the recombinant strains by mixing 750 μ l of bacterial culture and 250 μ l of sterile 80% glycerol. Plasmids were extracted from overnight cultures through NucleoSpin Plasmid kit (Macherey-Nagel). DNA was digested as appropriate and the fragments of interest were extracted from 1% agarose gel by NucleoSpin Extract II kit (Macherey-Nagel) before proceeding with ligation to ultimate the assembly. DNA-modifying enzymes were purchased from Roche Diagnostics and used according to manufacturer's instructions. DNA sequencing of plasmids and PCR products was performed through BMR Genomics (Padova, Italy) DNA analysis service.
MG-HSL RFP , MG-101 RFP and MG-IQ RFP were obtained by integrating HSL RFP , 101 RFP and IQ RFP into the MG1655 host (through pϕ 80-HSL RFP , pϕ 80-101 RFP and pϕ 80-IQ RFP integrative plasmids, respectively), as described in the BBa_K300000 integrative vector page in the Registry of Standard Biological Parts . After genomic integration (mediated by the pInt80-649 helper plasmid ) and marker excision (mediated by the pCP20 helper plasmid ), only one copy of the expression device of interest remains in the E. coli chromosome and it is flanked by four transcriptional terminators to achieve isolation from genomic context. No antibiotic resistance is present at the end of the procedure, so the resulting recombinant strains are always grown in nonselective medium. Integrants were PCR-verified with primers P1 (5'-CTGCTTGTGGTGGTGAAT-3') and P4 (5'-TAAGGCAAGACGATCAGG-3'), annealing in the genome . Platinum Taq DNA polymerase (Invitrogen) was used for PCR. When required, amplified fragments were gel-extracted as described above and sequenced.
Population-based fluorescence assays
Recombinant strains were grown at 37°C, 220 rpm for about 16 hours in selective M9 supplemented medium (11.28 g/L M9 salts, 1 mM thiamine hydrochloride, 2 mM MgSO4, 0.1 mM CaCl2, 0.2% casamino acids and 0.4% glycerol as carbon source) , inoculated with a single colony from a streaked selective LB-agar plate. The cultures were 100-fold diluted in fresh selective medium and grown under the same conditions for 6 hours, then they were diluted at the optical density at 600 nm (OD600) of 0.03 and grown for 45 minutes. For the cultures that did not need to be induced, 200 μ l were transferred into a flat-bottomed 96-well microplate (Greiner) and incubated in the Infinite F200 (Tecan) reader. For the cultures bearing the HSL-inducible system, 200 μ l for each investigated HSL concentration were transferred and 2 μ l of properly diluted HSL (Sigma Aldrich) were added to the culture before incubation to yield the desired concentration. A kinetic cycle, programmed with the i-control™software (Tecan), was used to assay the cultures: every 5 min, 15 s linear shaking (3 mm amplitude), 5 s wait, absorbance measurement, fluorescence measurement were performed. RFP detection was carried out with 535/620 nm filters for excitation/emission and acquisition gain as appropriate, ranging from 50 to 100. GFP-expressing cells were assayed through the same procedure, but using the 485/540 nm filters for fluorescence acquisition. Together with the cultures of interest, 200 μ l of M9 supplemented medium and a non-fluorescent MG1655 culture were also included in each experiment to measure the background of absorbance and fluorescence respectively. A preliminary evaluation of the measurement system was carried out and results are reported in the Additional file 1 (Validation of the measurement system).
Recombinant strains expressing RFP or GFP were grown as described above and induced with HSL when required. After 2/3 hours from the induction, cells were either spread on a glass slide (for microscopy analysis) or properly diluted in sterile PBS (for flow cytometric analysis).
RFP-expressing cells were analyzed with an Olympus BX51 microscope with standard fluorescence equipment (HBO100W/2 lamp). Microphotographs were taken using an Olympus Camedia C-4040 digital camera. Fluorescence was detected through the green excitation performed with a 530-560 nm band pass excitation filter and a 590 nm dichroic mirror combined with a long pass barrier filter at 620 nm. UPlanFl 40× objective was used for all the acquisitions.
GFP-expressing cells were analyzed through a Partec PAS II flow cytometer equipped with an argon ion laser using the 488 nm blue line for excitation. Fluorescence emission was collected in FL1 by means of a 515-545 nm band pass filter. 100,000 events were collected and stored for each sample.
Raw absorbance and fluorescence time series were processed with the MATLAB 2007b suite (MathWorks, Natick, MA) to obtain doubling time, average RFP synthesis rate per cell (S cell ) and RPU during the exponential growth phase for each culture. The background absorbance time series of M9 supplemented medium was subtracted from each culture of interest to obtain the actual OD600 of bacterial cells.
Similarly, the fluorescence background time series was subtracted from the raw fluorescence of each culture to yield the actual RFP fluorescence of the bacterial population in the microplate well. Exponential growth phase was identified by visual inspection as the linear region of the ln(OD600) time series and the slope m of this line was computed with linear regression to yield the cell growth rate. Doubling time was computed as ln(2)/m. A signal proportional to the RFP synthesis rate per cell was computed as the numeric time derivative of RFP time series, divided by OD600 over time. This signal was averaged over the exponential growth phase, starting after 50 min from the induction time, to obtain the S cell of a culture [37, 48]. S cell value is expressed in Arbitrary Units of RFP per minute per cell (AU min-1 cell-1) at a specific gain, which represent an absolute unit . Relative units were computed as RPU x = Scell,x/S cell, ref , where x is the culture of interest and ref is the culture bearing the standard reference promoter with RFP (101 RFP ), in the same copy number conditions as the culture of interest and acquired with the same gain factor . Data analysis for GFP-expressing cells was conducted with the same procedure. Induction curves were fitted with the Hill equation , where V max is the maximum activity, Y can be either the S cell or the RPU of a system and I can be either the HSL concentration or the intracellular level of the HSL-LuxR activator complex, depending on the specific application. Basal activity of the luxI promoter was omitted from the Hill equation because its entity was much lower than V max in all the curves. The least squares fitting was performed through the MATLAB lsqnonlin routine. Among the presented results, S cell were shown for a set of cultures expressing RFP. Because a wide range of fluorescence intensities is produced by the studied cultures and then to have a good sensitivity in the acquisition process the fluorescence acquisition gain was tuned accordingly, S cell results had to be reported to the same gain (in this work a gain = 50 was chosen) to enable comparisons among values. To this aim, the fluorescence of a MG1655 culture bearing pHC-HSL RFP , grown for 6 hours in presence of 100 nM of HSL and diluted to OD600 = 0.01, was measured with different gain factors (50, 60, 70, 80, 90 and 100), set via i-control™software. All the collected measurements were divided by the RFP raw value at gain = 50, to compute RFPnorm,50and a calibration curve of gain vs RFPnorm,50was obtained (data not shown). Thus, S cell values computed from data acquired at gain = β were reported to gain = 50 by dividing them by the right conversion factor between β and 50, i.e. the RFPnorm,50value of the calibration curve corresponding to gain β.
Bright-field pictures acquired from the microscope were processed through the ImageJ software (Wayne Rasband, NIH) to enhance the contrast of photographed cells when required. Fluorescence images were not processed in any way.
Flow cytometric data analysis was carried out with the FloMax (Partec, Munster, Germany) software.
Species and parameters included in the mathematical model.
Parameter or species
LuxR protein concentration per cell
LuxR-HSL activated complex concentration per cell
RFP per cell
DNA copy number
LuxR synthesis rate per cell per DNA copy
AU min-1 cell-1
Maximum RFP synthesis rate per cell per DNA copy
AU min-1 cell-1
LuxR protein degradation rate
Cell growth rate
Dissociation constant of HSL-LuxR
Dissociation constant of HSL-LuxR complex and luxI promoter
Hill cooperativity constant of HSL-LuxR
Hill cooperativity constant of HSL-LuxR complex and luxI promoter
Per-cell fluorescence of constitutive RFP-producing systems as a function of copy number
Characterization of J23101 and lacIQ promoters in absolute units and indirect copy number estimation
S cell [AUcell-1min-1]
Estimated copy number
Induction curves of the HSL-inducible system in different copy number conditions as a function of HSL
Doubling times of the studied cultures.
Doubling time [min]
Measurement of the activity of the luxI promoter in different copy number conditions as a function of the activated complex
Estimated values for the V max , α lux , K A and n A parameters.
V max [RPU]
K A [AU cell-1]
α lux [AU min-1 cell-1]
Because the design of genetic circuits which exhibit a predictable behaviour is the basis of the enormous potential of synthetic biology, the investigation of nonlinear phenomena and hard-to-predict engineered cell responses is essential to understand the systems under study and to avoid time- and cost-consuming strategies like trial-and-error approaches. Many variables, such as promoter strength, ribosome binding site efficiency and plasmid copy number, can play a crucial role in systems design. The optimization of a genetic circuitry can be achieved by tuning these elements, but the findings described in literature disclosed nonlinear effects [25, 39]. The copy number of the DNA encoding the engineered genetic network is an important parameter for the regulation of gene dosage and many works reported its tuning to optimize biological circuits such as biosynthetic pathways . However, as it happens with other biological parameters, the linearity of the output of a circuit may not be valid in many copy number ranges, yielding difficult-to-predict saturation effects. Hajimorad et al. studied the mRNA output of simple, independent gene expression cassettes as a function of the copy number, disclosing copy number ranges and conditions in which the superposition of the effects, typical of linear systems, is valid for the studied devices. Here, the aim of our work is to analyze the induction curves produced by an HSL-inducible system as a function of the copy number conditions, considering the fluorescence as the output. The studied system is composed by a luxR constitutive expression cassette and a RFP expression cassette regulated by the luxI promoter, which can be turned on by LuxR in presence of HSL with a concentration-dependent fashion. This device has been previously used in several works [14, 37, 41] and the improvement of its characterization in different copy number contexts can be useful for the rational design of expression systems and cell-cell communication networks involving HSL [16, 17, 19]. In this work, high, medium, low and single copy number conditions were considered. Medium-strength constitutive promoters contained in these plasmids or integrated in the genome exhibit activities that vary more than 100-fold from single to high copy number contexts, which demonstrates that copy number variation can be actually achieved with the used vectors. Moreover, for each constitutive promoter, the fold-change of the fluorescence of plasmid-bearing strains, relative to the fluorescence of the recombinant strain with the device in single copy (assuming a copy number = 1 by definition), is in accordance with the theoretical copy number of the used vectors. The strength of the HSL-inducible system was experimentally measured in vivo and expressed in RPUs, which are standard units for promoter activity evaluation proposed in literature to lower the variability among experiments. They also enable the sharing of the promoter characterization results in the synthetic biology community, as all the activity measurements are performed relative to the activity of a standard reference promoter (J23101), which gives RPUs = 1 by definition. Induction curves were obtained in different copy number conditions by exogenously adding HSL to the inducible system under investigation. A saturating trend in the maximum strength was depicted for higher copy number conditions, probably due to limited availability of polymerases or ribosomes which brings transcription/translation processes to saturation, as already reported in literature for other gene expression systems [39, 42]. It is worth noting that, in addition to the luxI promoter strength, the presence of the luxR expression cassette may contribute to the saturating trend of the luxI promoter maximum relative activity in the higher copy number conditions by increasing the metabolic demand of the device. The same effect was seen for similar recombinant strains with the HSL-inducible device expressing a different fluorescent reporter gene (GFP instead of RFP), thus demonstrating that such phenomenon was not specific for the RFP gene. Characterization of the HSL-RPU transfer function was completed in each copy number context by fitting the curve with a Hill function. As they are expressed in standard units, the results produced here support the re-use of this inducible device, given the specific copy number in which it was characterized. However, these Hill curves do not directly represent the transfer functions of the luxI promoter, since it is not simply activated by HSL, but by the activator complex HSL-LuxR. The number of DNA copies of the luxR constitutive cassette varied in concert with the copy number of the rest of the device, so the copy number of this cassette leads to different availability of intracellular LuxR proteins in the different studied contexts. In order to obtain comparable transfer functions among different copy number conditions, an X-axis transformation on the HSL-RPU induction curves is required to express the promoter strength as a function of the concentration of the actual inducer. A model-based approach has been proposed to indirectly measure the HSL-LuxR activated complex concentration by predicting the intracellular availability of LuxR protein as a function of the expected copy number. This procedure allowed to express the model output (RPUs) as a function of the HSL-LuxR complex (called A). With the help of the mathematical model, the values of the promoter activity curves can be considered as a function of the correct inducer of the promoter, thus allowing comparisons of transcriptional strength in all the studied contexts. The relationship between K A (i.e. the dissociation constant between A and the luxI promoter) and the copy number shows correlation (see Figure 4), while the K m values found in the input-output induction curves simply as a function of HSL, showed no apparent correlation with the copy number (see Figure 2). However, it is important to note that the used model relies on parameters that have been indirectly estimated in this study (e.g. the plasmid copy number and intracellular concentration of LuxR protein). Moreover, this indirect estimation assumes that the luxR gene expression level per DNA copy (α tet ) is constant for each copy number, neglecting any saturation effect in high copy number conditions. Even if more in-depth analysis of the biological systems under study can be performed to measure these parameters and to obtain more accurate relationships, the found behaviour for which the K A parameter value appears to correlate with the copy number is still valid, since the monotonically increasing trend of K A was confirmed even after a sensitivity analysis in which the (α tet ) parameter was varied 2-fold (data not shown). Finally, a single-cell analysis was performed to improve the characterization of strains bearing the HSL-inducible device. From the literature, it is well known that in some inducible systems not all the cells among the population respond to the inducer, thus causing the simultaneous presence of induced/uninduced subpopulations [50, 51]. On the contrary, results on the HSL-inducible device characterized in this work show that all the cells respond to the induction. This is a very interesting feature for the future usage of such HSL-inducible device in designing more complex circuits.
In conclusion, the characterization in standard, sharable units of a simple synthetic biological device has been performed and the input-output transfer function of the whole device has been reported for different copy number conditions. Even if the device is not significantly complex, nonlinear phenomena were evident in the higher copy number conditions. Moreover, the individual study of the activity of the inducible promoter present in the device was not trivial and it required a model-based approach to indirectly estimate hard-to-measure intracellular species, whose knowledge was necessary to completely characterize the system behaviour and to allow comparisons among different copy number contexts.
List of abbreviations used
green fluorescent protein
messenger ribonucleic acid
polymerase chain reaction
ribosome binding site
red fluorescent protein
relative promoter unit.
The authors thank the anonymous reviewers for their useful suggestions. This project was partially funded by the Italian "Ministero dell'Università e della Ricerca" through the FIRB ITALBIONET project.
This article has been published as part of BMC Bioinformatics Volume 13 Supplement 4, 2012: Italian Society of Bioinformatics (BITS): Annual Meeting 2011. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/13/S4.
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