Engineering Success

Cycle 1: Uric Acid Standard Curve Plotting

Design

R (Reporter assay) Module: phucR - BBa_B0030 - mTagBF2 - B1005

• phucR is a composite element that includes a promoter, HucO operator, and RBS (ribosome binding site)[6].
• mTagBF2 is a blue fluorescent protein used to report gene expression, with Ex=399 nm and Em=454 nm [7].
• In the absence of uric acid, HucR binds to the HucO region of phucR, preventing the expression of mTagBF2.

A (Auxiliary system) Module: J23119-BBa_B0034 - HucR - YgfU - B1006

• J23119 is a constitutive promoter responsible for the co acid transporter protein that actively transportsntinuous expression of HucR and YgfU [6,8].
• HucR is a regulatory protein that binds to the HucO region on phucR, inhibiting the expression of mTagBF2 in the R module.
• YgfU is a uric acid transporter protein that actively transports uric acid from the environment into Escherichia coli cells[9].

When uric acid is present in the environment, it weakens the binding strength between HucR and HucO, causing HucR to dissociate from the HucO region, thus reducing its inhibitory effect on mTagBF2 expression. As HucR dissociates from HucO, the steric hindrance is relieved, allowing the transcription and expression of mTagBF2 in the R module, resulting in a blue fluorescent signal. By detecting the fluorescence intensity of the blue fluorescent protein (mTagBF2), the concentration of uric acid in the environment can be indirectly measured.

Build

All plasmids were obtained through full synthesis. After synthesis, the plasmids were transformed into DH5α cells and plated on antibiotic-selective plates to obtain single colonies. The single colonies were then inoculated in 5 ml LB medium, with part of the culture sent to Azenta for sequencing verification, while the remaining portion was preserved using 20% glycerol.

Test

From the preserved strains, streak them on LB plates with the corresponding antibiotic resistance to obtain single colonies. Select three single colonies and inoculate them into LB medium containing the appropriate antibiotic, then culture overnight. The next day, dilute the culture to the logarithmic growth phase and add different concentrations of uric acid PBS solution, continuing the culture for 16 hours. After incubation, take 150 μL of the culture into the TECAN plate reader to measure the fluorescence signal of mTagBF2 and the OD values.

Figure 1. The response of the HucR-YgfU-phucR system to uric acid solutions of different concentrations (0, 5, 10, 20, 50, 100, 200 µM).

Learn

In this experiment, the activated Hill equation model was used to fit the relationship between uric acid concentration and the response of the biosensor.

Figure 2. The Hill equation fitting curve representing the response of the HucR-YgfU-phucR system to uric acid solutions of varying concentrations (0, 5, 10, 20, 50, 100, 200 µM).

1.Fitting Parameters:

• Vmin=699.79

• Vmax=12,395.12

• K=72.97 mol^-1L

• n=3.17

• R²=0.9893

2.Analysis:

• High R² value: The R² value of 0.9893 indicates a very high degree of fit between the experimental data and the activated Hill equation model, suggesting that the model can effectively describe the relationship between uric acid concentration and the biosensor response.

• Hill coefficient (n): The value of (n = 3.17) implies positive cooperativity in the binding process of uric acid. This means that the binding of one uric acid molecule enhances the binding ability of subsequent molecules, leading to a more sensitive response of the biosensor within certain concentration ranges.

• Half-maximum effective concentration (K): The K value of 72.97 mol^-1L indicates that the biosensor's response reaches half of its maximum at a uric acid concentration of 72.97 mol^-1L. This reflects the sensitivity and optimal working range of the biosensor for uric acid.

• Dynamic range: The values of Vmax and Vmin are 12,395.12 and 699.79, respectively, demonstrating that the biosensor has a wide detection range for uric acid, capable of detecting from low to high concentrations.

Conclusion

The results indicate that our system exhibits the highest sensitivity to uric acid at a concentration of 72.97 mol·L⁻¹ and has a broad detection range, capable of detecting uric acid from low to high concentrations. Additionally, we found that the binding process of uric acid exhibits positive cooperativity, which means that the binding of uric acid molecules enhances the binding ability of subsequent molecules, leading to a more sensitive response of our system within certain concentration ranges.

Cycle 2: Glucose Standard Curve Validation

Design

PI（BBa_K861170）-B0030-mKate2-B1005

BBa_K861170 is a promoter designed for *Escherichia coli*. It contains a modified consensus CRP binding site and a consensus RNA polymerase binding site, with several overlapping base pairs. Due to spatial hindrance between CRP and RNA polymerase, the gene downstream of the promoter is repressed. In cells, low glucose concentration leads to increased activity of adenylate cyclase, which raises the concentration of cAMP in the cell. cAMP binds to CRP, and in its bound form, CRP tightly associates with specific DNA sites in the promoter, thereby inhibiting the expression of downstream genes. Conversely, high glucose concentration results in the expression of downstream genes[10].

mKate2 is a fluorescent protein with spectral frequencies of Em=588 and Ex=633. We cloned mKate2 downstream of BBa_K861170, allowing the expression of the fluorescent protein to be regulated by changes in glucose concentration in the environment. Additionally, its fluorescent signal can be distinguished from other biomarkers[11].

Build

All plasmids were obtained through full synthesis. After synthesis, the plasmids were transformed into DH5α cells and plated on antibiotic-selective plates to obtain single colonies. The single colonies were then inoculated in 5 ml LB medium, with part of the culture sent to Azenta for sequencing verification, while the remaining portion was preserved using 20% glycerol.

Test

From the preserved strains, streak them on LB plates with the corresponding antibiotic resistance to obtain single colonies. Select three single colonies and inoculate them into LB medium containing the appropriate antibiotic, then culture overnight. The next day, dilute the culture to the logarithmic growth phase and add different concentrations of glucose solution, continuing the culture for 16 hours. After incubation, take 150 μL of the culture into the TECAN plate reader to measure the fluorescence signal of mKate and the OD values.

Figure3. Response of the PI system to glucose solutions of varying concentrations (0, 1, 2, 4, 8, 16, 32, 64 mM).

Learn

In this experiment, the activated Hill equation model was used to fit the relationship between uric acid concentration and the response of the biosensor.

Figure 4. Hill equation fitting curve for the PI system's response to glucose solutions at varying concentrations.

Fitting Parameters:

• Vmin=4,023.77

• Vmax=45,740.73

• K=6.09 mol^-1L

• n=0.56

• R²=0.9711

Analysis:

• High R² Value: An R² value of 0.9711 indicates a high degree of fit between the experimental data and the activated Hill equation model, suggesting that the model provides a good description of the relationship between glucose concentration and the biosensor response.

• Hill Coefficient (n): A value of n=0.56 implies negative cooperativity in the binding process of glucose. This indicates that the binding of one glucose molecule diminishes the binding affinity for subsequent molecules, resulting in a more gradual response curve. This could be attributed to the fact that glucose, as the primary energy source for cells, may influence the biosensor's response due to its metabolic processes.

• Half-Maximum Effective Concentration (K): The K value of 6.09 mol^-1L signifies that the biosensor's response reaches half of its maximum at a glucose concentration of 6.09 mol^-1L. This reflects the biosensor's sensitivity and optimal operational range for glucose.

• Dynamic Range: The values of Vmax and Vmin being 45,740.73 and 4,023.77, respectively, demonstrate a broad detection range for glucose by the biosensor. However, further optimization is required to enhance sensitivity at lower concentrations.

Conclusion:

The results show that our system exhibits the highest sensitivity to glucose at a concentration of 6.09 mol^-1L and has a wide detection range. However, there is a need for further optimization to improve sensitivity at lower glucose concentrations. Additionally, the negative cooperativity observed in the binding process suggests that the binding of glucose molecules weakens the binding capacity for subsequent molecules, leading to a more gradual response curve. This could be due to the impact of glucose metabolism on the biosensor's response, given that glucose serves as the main energy source for cells.

Cycle 3: Standardization of Interfaces and Verification of Lactic Acid Standard Curve

Design

To enhance detection of additional biomarkers, we optimized our plasmid system by incorporating GoldenGate interfaces in key locations of Modules A and R. This improvement increases system flexibility for future integration of biological components, expanding detection capabilities. We validated the standardized interface assembly of Module R using lactic acid.

We introduced two BsaI restriction sites in Module R, one at the promoter and another at the fluorescent protein site, allowing for the selection of sensitive promoters for new biomarkers and swapping fluorescent proteins for different detection channels, enabling simultaneous multi-biomarker detection.

The control vector uses a promoter (PI) and a red fluorescent protein (mKate2) from cycle2, which naturally emits red fluorescence in glucose-containing media, serving as a basis for assembly success.

R (Reporter assay) Module: ALPaGA -BBa_B0030-sfGFP - B1005

ALPaGA, a lactic acid-sensitive promoter, was selected for its ability to respond to lactic acid in high glucose environments, a biomarker of interest. sfGFP, with Excitation/Emission maxima at 465/515 nm, is positioned downstream of ALPaGA to detect lactic acid levels and differentiate from other biomarker signals [12].

Build

We synthesized the ALPaGA and sfGFP sequences with a standardized adapter and inserted these two sequences into the vector using the GoldenGate assembly protocol. They were transformed into DH5α and monoclonal clones without red fluorescence were selected on plates and sent to Anshengda for verification with Sanger sequencing. The correct bacterial strains were preserved in 20% glycerol at -80°C.

From the preserved bacterial strains, streak culture was performed on LB plates with the corresponding antibiotic resistance to obtain monoclonal clones. Three monoclonal bacterial strains were selected and inoculated into LB broth with the appropriate antibiotic resistance and cultured overnight. The following day, the culture was diluted to the logarithmic growth phase, and different concentrations of glucose solution were added and continued to be cultured for 16 hours. After the culture was completed, 150 μL of the culture broth was taken into the TECAN plate reader to measure the mKate fluorescence signal and OD value.

Test

From the preserved strains, streak them on LB plates with the corresponding antibiotic resistance to obtain single colonies. Select three single colonies and inoculate them into LB medium containing the appropriate antibiotic, then culture overnight. The next day, dilute the culture to the logarithmic growth phase and add different concentrations of glucose solution, continuing the culture for 16 hours. After incubation, take 150 μL of the culture into the TECAN plate reader to measure the fluorescence signal of mKate and the OD values.

Figure 5. Hill equation fitting curve for the ALPaGA system's response to lactic acid solutions at varying concentrations (0, 0.25, 0.5, 1, 2.5, 5, 10, 20, 40 µM).

Learn

In this experiment, the activated Hill equation model was used to fit the relationship between uric acid concentration and the response of the biosensor.

Figure 6. Hill equation fitting curve for the ALPaGA system's response to lactic acid solutions at varying concentrations (0, 0.25, 0.5, 1, 2.5, 5, 10, 20, 40 µM).

1.Fitting Parameters:

• Vmin=443.89

• Vmax=1,992.40

• K=4.14 mol^-1L

• n=1.85

• R²=0.9939

2.Analysis:

• High R² Value: An R² value of 0.9939 indicates an extremely high degree of fit between the experimental data and the activated Hill equation model, suggesting that the model can accurately describe the relationship between lactic acid concentration and the biosensor response.

• Hill Coefficient (n): A value of n=1.85 implies positive cooperativity in the binding process of lactic acid. This indicates that the binding of one lactic acid molecule enhances the binding affinity for subsequent molecules, leading to a more sensitive response of the biosensor within certain concentration ranges.

• Half-Maximum Effective Concentration (K): The K value of 4.14 mol^-1L signifies that the biosensor's response reaches half of its maximum at a lactic acid concentration of 4.14 mol^-1L. This reflects the biosensor's sensitivity and optimal operational range for lactic acid.

• Dynamic Range: The values of Vmax and Vmin being 1,992.40 and 443.89, respectively, demonstrate a relatively narrow detection range for lactic acid by the biosensor. However, the response curve is quite steep, making it suitable for high-sensitivity detection within a specific concentration range.

Conclusion:

The results show that our system exhibits the highest sensitivity to lactic acid at a concentration of 4.14 mol^-1L and has a narrow detection range but with a steep response curve, making it suitable for high-sensitivity detection within a specific concentration range. Additionally, the positive cooperativity observed in the binding process suggests that the binding of lactic acid molecules enhances the binding capacity for subsequent molecules, leading to a more sensitive response of our system to lactic acid within certain concentration ranges.

Cycle 4: Standardized Interface and Tryptophan Standard Curve Verification

Design

We've introduced a BsaI restriction site in Module A to accommodate biomarkers that require auxiliary proteins for detection. This site is located between BBa_B0034 and BBa_B1006, allowing for simple construction with synthesized proteins featuring the interface. We selected tryptophan as the biomarker for validation.

Through literature research, we learned that E. coli has a native TrpR-pTrp system for sensing tryptophan levels, which regulates its own tryptophan synthesis pathway. We cloned TrpR into Module flag and TrpO into Module R, followed by sfGFP for reporting. In the presence of tryptophan, TrpR binds to TrpO, blocking the transcription and translation of the fluorescent protein downstream. In the absence of tryptophan, this pathway is open, allowing downstream gene expression and fluorescence protein production[13].

A (Auxiliary system) Module: J23119-BBa_B0034 - TrpR - B1006

R (Reporter assay) Module: pTrpR -BBa_B0030-sfGFP - B1005

Build

We synthesized gene fragments with standard interfaces and assembled them into the designed standard vector using the GoldenGate method. These were transformed into DH5α, and three monoclonal colonies were selected for sequencing at Anshengda. The remaining bacterial culture was preserved with 20% glycerol.

Test

From the preserved strains, monoclonal cultures were streaked on LB plates with corresponding antibiotic resistance to obtain monoclonals. Three monoclonal bacterial strains were selected and inoculated into LB broth with the appropriate antibiotic resistance and cultured overnight. The next day, the culture was diluted into the logarithmic growth phase, and different concentrations of tryptophan solution were added for continued culture for 16 hours. After cultivation, 150 μL of the culture broth was taken into the TECAN plate reader to measure the mKate fluorescence signal and OD value.

Figure 7. Response of the TrpR-pTrp system to tryptophan solutions at varying concentrations (0, 0.1, 0.25, 0.5, 1, 2, 4 mM).

Learn

Figure 8. Hill equation fitting curve for the TrpR-pTrp system's response to tryptophan solutions at varying concentrations (0, 0.1, 0.25, 0.5, 1, 2, 4 mM).

1.Fitting Parameters:

• Vmin=186.39

• Vmax=134,991.95

• K=0.03 mol^-1L

• n=1.11

• R²=0.9594

2.Analysis:

• High R² value: The R² value of 0.9594 indicates a high degree of fit between the experimental data and the inhibitory Hill equation model, suggesting that the model can effectively describe the relationship between tryptophan concentration and the biosensor response.

• Hill Coefficient (n): A value of n=1.11 implies that the binding process of tryptophan is close to non-cooperative. This means that the binding of one tryptophan molecule has a minimal effect on the binding capacity of subsequent molecules, resulting in a more gradual response curve.

• Half-maximum effective concentration (K): The K value of 0.03 mol^-1L indicates that the biosensor's response reaches half of its maximum at a tryptophan concentration of 0.03 mol^-1L. This reflects the biosensor's high sensitivity and optimal working range for tryptophan.

• Dynamic Range: The values of Vmax and Vmin being 134,991.95 and 186.39, respectively, demonstrate a very wide detection range for tryptophan by the biosensor, capable of detecting from extremely low to high concentrations of tryptophan.

Conclusion:

The results show that our system exhibits the highest sensitivity to tryptophan at a concentration of 0.03 mol^-1L and has an extremely wide detection range, capable of detecting tryptophan from very low to high concentrations. Additionally, the binding process of tryptophan is close to non-cooperative. This means that the binding of tryptophan molecules has a minimal impact on the binding capacity of subsequent molecules, leading to a more gradual response curve.

Cycle 5: Fluorescence Crosstalk Experiment

Design

Our hardware team's adoption of a triple-well fluidic pathway design has led us to select three fluorescent proteins with distinct excitation-emission wavelength profiles: mTagBF2, sfGFP, and mKate2. To ensure the accuracy and effectiveness of our system, we are currently validating whether these three fluorescent proteins exhibit any signal crosstalk under experimental conditions. This is to rule out potential fluorescent interference issues and ensure the reliability of the detection results.

We have cloned the three fluorescent proteins: mTagBF2, sfGFP, and mKate2 into the same pUC57 kana vector, driven by the same promoter (J23119) and RBS (B0034).

Build

We synthesized the coding genes for these fluorescent proteins using a fully synthetic approach. They were then individually transformed into DH5α and cultured on corresponding plates to obtain monoclonal colonies.

Test

1. Three monoclonal colonies were selected and cultured in 5 ml of LB broth with the corresponding antibiotic resistance overnight. The following day, they were tested using a TECAN Spark.

2. The excitation wavelength specific to mTagBFP2 was used to excite mTagBFP2, sfGFP, and mKate2 sequentially, recording their respective emission spectra.

3. The excitation wavelength specific to sfGFP was used to excite the three fluorescent proteins sequentially, recording the emission spectra.

4. The excitation wavelength specific to mKate2 was used to excite the three fluorescent proteins sequentially, recording the emission spectra.

Learn

Figure 9. The Impact of Three Types of Excitation Light on Three Fluorescent Proteins.
Blue Fluorescence Channel: Excitation at 399 nm, Emission at 455 nm.
Green Fluorescence Channel: Excitation at 465 nm, Emission at 525 nm.
Red Fluorescence Channel: Excitation at 588 nm, Emission at 635 nm.

When different excitation lights are used to excite the three fluorescent proteins, the crosstalk effect between them is observed to be weak:

1. When excited with the excitation light for sfGFP, the emission intensity of sfGFP is significantly higher than the other two fluorescent proteins, with an emission intensity more than 100 times that of the others.

2. When excited with the excitation light for mTagBFP2, the emission intensity of mTagBFP2 is also markedly higher than the other two fluorescent proteins, with an emission intensity more than 20 times that of the others.

3. When excited with the excitation light for mKate2, the emission intensity of mKate2 is the most prominent, with an emission intensity more than 800 times that of the other fluorescent proteins.

These results indicate that when excited by their specific excitation lights, each fluorescent protein primarily exhibits its own emission characteristics, with minimal crosstalk between the three. Therefore, for future use in the same hardware, we can utilize these three proteins.

Referrence

1. https://2019.igem.org/Team:QHFZ-China/Description
2. https://2019.igem.org/Team:Hong_Kong_LFC_PC/Design
3. https://2017.igem.org/Team:Hong_Kong_UCCKE
4. https://2018.igem.org/Team:DLUT_China/Description
5. https://2022.igem.wiki/sesame-shenzhen/proof-of-concept
6. Geraths, C., Christen, E.H. & Weber, W., 2012. A hydrogel sensing pathological urate concentrations. Macromolecular Rapid Communications, 33(24), pp.2103-2108. doi:10.1002/marc.201200563.
7. Subach, O.M., Cranfill, P.J., Davidson, M.W. & Verkhusha, V.V., 2011. An enhanced monomeric blue fluorescent protein with the high chemical stability of the chromophore. PLoS One, 6(12), p.e28674. doi:10.1371/journal.pone.0028674.
8. Yan, Q. & Fong, S.S., 2017. Study of in vitro transcriptional binding effects and noise using constitutive promoters combined with UP element sequences in Escherichia coli. Journal of Biological Engineering, 11, p.33. doi:10.1186/s13036-017-0075-2.
9. Papakostas, K. & Frillingos, S., 2012. Substrate selectivity of YgfU, a uric acid transporter from Escherichia coli. Journal of Biological Chemistry, 287(19), pp.15684-15695. doi:10.1074/jbc.M112.355818.
10. Zhang, L.Y., Lin, R.T., Chen, H.R., et al., 2021. High glucose activated cardiac fibroblasts by a disruption of mitochondria-associated membranes [retracted in: Frontiers in Physiology, 2022, 13, p.1006459]. Frontiers in Physiology, 12, p.724470. Published on 18 August 2021.
11. Shemiakina, I., Ermakova, G., Cranfill, P. et al., 2012. A monomeric red fluorescent protein with low cytotoxicity. Nature Communications, 3, p.1204. doi:10.1038/ncomms2208.
12. Zúñiga, A., Camacho, M., Chang, H.J., et al., 2021. Engineered l-lactate responding promoter system operating in glucose-rich and anoxic environments. ACS Synthetic Biology, 10(12), pp.3527-3536. doi:10.1021/acssynbio.1c00456.
13. Chevalet, L., Robert, A., Gueneau, F., Bonnefoy, J.Y. & Nguyen, T., 2000. Recombinant protein production driven by the tryptophan promoter is tightly controlled in ICONE 200, a new genetically engineered E. coli mutant. Biotechnology and Bioengineering, 69(4), pp.351-358.