iGEM 2024 — Team lcg-global

{% extends "layout.html" %}
{% block extraStyles %}

{% endblock %}
{% block title %}Notebook{% endblock %}
{% block lead %}Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.{% endblock %}
{% block page\_content %}

# Notebook

## **LCG-Global iGEM Team Project Timeline**

**LCG-Global iGEM team** initiated their project in **early 2024**, focusing on the development of **biosensors** for detecting biomarkers such as **uric acid**, **glucose**, **lactic acid**, and **tryptophan**. Following team formation in **late February**, foundational training was conducted in **April**, covering **synthetic biology** and **iGEM competition principles**. In **May**, the team advanced to detailed project design, including **plasmid construction**. By **June**, plasmid designs were finalized, and synthesis requests were submitted. The formal experimentation phase began in **July**, involving **plasmid verification** and testing of **biosensor functionality** across several weeks. The team successfully established **detection systems** for the biomarkers and minimized **fluorescence crosstalk**. By **September** and **October**, data analysis and preparations for the **iGEM Jamboree** were completed, showcasing the team's progress and innovative contributions to **synthetic biology**.

![test tube](https://static.igem.wiki/teams/4477/wiki/images/test-tube.svg)

## LCG-Global 2024 Notebook

![](https://static.igem.wiki/teams/5460/firstpage/experiments-2.jpg)

A glimpse of our wet-lab daily works

#### Notebook in Detail

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a part of your project by going through at least one iteration of the engineering design cycle.{% endblock %}
{% block page\_content %}

# Engineering Success

## **Achieving Engineering Success of a Versatile Biomarker Detection System**

In our project, **bioengineering** assumes a pivotal role, responsible for the design and experimental development of a versatile biosensor system, ensuring its performance and precision. As the technical cornerstone, bioengineering provides the essential biological detection functions for the hardware system. Beyond designing the biological components of the sensor, it rigorously validates the sensor's effectiveness and stability in detecting multiple biomarkers simultaneously, safeguarding against crosstalk during integration with the hardware. Thus, bioengineering not only serves as the scientific foundation of the project but also collaborates closely with hardware engineering to achieve the overarching objectives.

Our bioengineering successfully developed a **modular detection system for various biomarkers**, innovatively using two plasmid modules: the Auxiliary (A) system and the Reporter assay (R) module. The A module facilitates the entry of non-permeable biomarkers into *E. coli* with auxiliary proteins, while the R module quantifies their effects using fluorescent proteins.

Our **validation confirmed the system's effectiveness** across **multiple biomarkers**, demonstrating a versatile platform suitable for expanding biomarker detection capabilities in synthetic biology.

Overview

Cycle 1: Uric Acid Standard Curve Plotting

Design

Build

Test

Learn

Cycle 2: Glucose Standard Curve Validation

Design

Build

Test

Learn

Cycle 3: Lactic Acid Standard Curve Validation

Design

Build

Test

Learn

Cycle 4: Standardized Interface and Tryptophan Standard Curve Verification

Design

Build

Test

Learn

Cycle 5: Fluorescence Crosstalk Experiment

Design

Build

Test

Learn

Referrence

## Overview

Our Human Practices established the overarching objectives for the project, dividing the specific engineering tasks into two components: bioengineering and hardware engineering, which require close integration and coordination. In the bioengineering component, the aim is to develop a versatile biosensor system through bioengineering design and wet lab experiments, while rigorously validating the absence of crosstalk during the simultaneous detection of multiple biomarkers.

![](https://static.igem.wiki/teams/5460/engineering/engineering-blueprint.png)

Bioengineering's role in our project's bluprint(enclosed by red box)

Through our peer review of previous iGEM teams, we found many outstanding teams in the past have developed various detection systems to identify a range of biomarkers, which can include environmental heavy metals, proteins related to human health, or small molecules linked to food safety[1-5]. The mechanisms of action generally fall into two categories:

1. **Direct mechanism:** The biomarker can directly cross the cell membrane and act on a specific sensitive promoter, thereby starting or stopping the expression of downstream proteins.
2. **Indirect mechanism:** The biomarker cannot directly cross the membrane or may need to interact with a protein first, forming a complex that then activates or terminates downstream expression.

We summarized the designs of these teams and developed our own system. In our system, we designed two plasmids, dividing them into two types and abstracting two functional modules:

1. **A (Auxiliary system) module:**
   1. This module is primarily responsible for encoding auxiliary proteins, such as transport proteins and intermediate proteins. Since some biomarkers cannot directly cross the cell membrane, transport proteins are required to help these biomarkers enter the *E. coli* cells.
   2. Additionally, these biomarkers may need to first form a complex with certain proteins before they can act on the sensitive promoter and initiate downstream gene expression.
   3. To achieve these functions, we used a strong promoter to express auxiliary proteins, while also designing standard Golden Gate interfaces to allow future replacement of auxiliary proteins, enhancing the flexibility and adaptability of the module.
2. **R (Reporter assay) module:**
   1. This module is mainly responsible for generating the signal, typically achieved by encoding a fluorescent protein downstream of a sensitive promoter. By detecting the fluorescent signal, the impact of the biomarker on the bacteria can be evaluated.
   2. Standard Golden Gate interfaces were also designed upstream and downstream of the sensitive promoter, facilitating other research teams in constructing their own sensitive promoters, thus promoting further research.

We conducted the following engineering validations based on this design:

1. To validate the performance of the system, we tested it against three different biomarkers to ensure their feasibility and effectiveness under preset conditions.
2. To enable simultaneous detection, we loaded three different fluorescent proteins (mTagBF2, sfGFP, and mKate2) into the R modules for the three biomarkers, and verified that their signals could be accurately detected.
3. To ensure that there was no crosstalk between these three fluorescent proteins in the designed hardware system, we designed and conducted a fluorescence crosstalk experiment, specifically testing their crosstalk at different excitation-emission wavelengths.
4. To enhance the system's flexibility, we designed a standard interface and, through modular assembly via this interface, successfully validated the ability to test a fourth biomarker, further expanding the system’s range of applications.

## 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.

![](https://static.igem.wiki/teams/5460/engineering/add1.png)

**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].

![](https://static.igem.wiki/teams/5460/engineering/add2.png)

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.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-1.jpg)

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.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-2.jpg)

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**

![](https://static.igem.wiki/teams/5460/engineering/add3.png)

**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.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-3.jpg)

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.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-4.jpg)

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**

![](https://static.igem.wiki/teams/5460/engineering/add4.png)

**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.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-5.jpg)

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.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-6.jpg)

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

![](https://static.igem.wiki/teams/5460/engineering/add5.png)

R (Reporter assay) Module: pTrpR -BBa\_B0030-sfGFP - B1005

![](https://static.igem.wiki/teams/5460/engineering/add6.png)

#### 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.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-7.jpg)

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

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-8.jpg)

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

![](https://static.igem.wiki/teams/5460/engineering/add7.png)

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](https://2019.igem.org/Team%3AQHFZ-China/Description)
2. [https://2019.igem.org/Team:Hong\_Kong\_LFC\_PC/Design](https://2019.igem.org/Team%3AHong_Kong_LFC_PC/Design)
3. [https://2017.igem.org/Team:Hong\_Kong\_UCCKE](https://2017.igem.org/Team%3AHong_Kong_UCCKE)
4. [https://2018.igem.org/Team:DLUT\_China/Description](https://2018.igem.org/Team%3ADLUT_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.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
{% block page\_content %}

# Experiments

LB Preparation

LB Agar Plate Preparation

Antibiotic Stock Solution Preparation

Kanamycin Stock Reagent

Chloramphenicol Stock Reagent

Preparation of Different Biomarker Stock Solutions

Uric Acid

Lactic Acid

Tryptophan

Glucose

Preparation of Biomarker Induction Solutions at Different Concentrations

Uric Acid

Lactic Acid

Tryptophan

Glucose

Bacterial Transformation

Bacterial Seed Culture Preparation, Induction, and Fluorescence Assay Testing

Fluorescent Protein Standard Expression and Purification

SDS-PAGE

## LB Preparation

1. Take one bottle of BeyoPure™ LB Broth (Catalog No. ST156), open the package, and add it entirely into a 1L blue-capped bottle.
2. Add ultrapure water from the Milli-Q® Type 1 Ultrapure Water System until it reaches 500 ml, shake well, apply autoclave tape, and slightly loosen the bottle cap.
3. Sterilize at 121°C for 30 minutes.

## LB Agar Plate Preparation

1. Take one bottle of BeyoPure™ LB Broth with Agar (Catalog No. ST158), open the package, and add it entirely into a 1L blue-capped bottle.
2. Add ultrapure water from the Milli-Q® Type 1 Ultrapure Water System until it reaches 500 ml, shake well, apply autoclave tape, and slightly loosen the bottle cap.
3. Sterilize at 121°C for 30 minutes.
4. Allow it to cool to around 60°C, then add the corresponding antibiotic.

## Antibiotic Stock Solution Preparation

#### 1. Kanamycin Stock Reagent

1. Weigh 1 g of kanamycin powder (Beyotime: ST101) and add it into a 50 ml centrifuge tube.
2. Add 20 ml of sterilized ddH2O (Sangon Biotech: B541017), vortex to mix, let it stand for 3 minutes, and filter sterilize using a sterile membrane filter (Merck Millex™-GS Sterile Syringe Filter Unit, MCE, 0.22 μm).
3. 50 mg/ml kanamycin stock solution was prepared as described above and subsequently aliquoted into 2 ml EP tubes for storage.

#### 2. Chloramphenicol Stock Reagent

1. Weigh 600 mg of chloramphenicol powder (Beyotime: ST2722-1g) and add it into a 50 ml centrifuge tube.
2. Add 20 ml of sterilized ddH2O (Sangon Biotech: B541017), vortex to mix, let it stand for 3 minutes, and filter sterilize using a sterile membrane filter (Merck Millex™-GS Sterile Syringe Filter Unit, MCE, 0.22 μm).
3. 30 mg/ml Chloramphenicol stock solution was prepared as described above and subsequently aliquoted into 2 ml EP tubes for storage.

## Preparation of Different Biomarker Stock Solutions

#### 1.Uric Acid

* 50 ml of 1X PBS (Beyotime C0221A) was added to a 50 ml centrifuge tube, followed by the addition of 0.0168 g of uric acid. The mixture was vortexed, allowed to stand for 3 minutes, and then sterilized by filtration using a 0.22 μm sterile membrane filter (Merck Millex™-GS Sterile Syringe Filter Unit, MCE). The resulting solution was aliquoted into 2 ml EP tubes for storage.

#### 2.Lactic Acid

* 0.2 g quantity of 90% lactic acid solution was pipetted into a 50 ml centrifuge tube, and ultrapure water from the Milli-Q® Type 1 Ultrapure Water System was added to a final volume of 50 ml. The solution was vortexed, allowed to stand for 3 minutes, and then sterilized by filtration using a 0.22 μm sterile membrane filter (Merck Millex™-GS Sterile Syringe Filter Unit, MCE). The resulting solution was aliquoted into 2 ml EP tubes for storage.

#### 3.Tryptophan

* 0.1 g quantity of tryptophan was weighed and added to a 50 ml centrifuge tube, and ultrapure water from the Milli-Q® Type 1 Ultrapure Water System was added to a final volume of 50 ml. The mixture was vortexed, allowed to stand for 3 minutes, and then sterilized by filtration using a 0.22 μm sterile membrane filter (Merck Millex™-GS Sterile Syringe Filter Unit, MCE). A 1 mM tryptophan stock solution was thus prepared and aliquoted into 2 ml EP tubes for storage.

#### 4.Glucose

* 1.8 g quantity of glucose was weighed and added to a 50 ml centrifuge tube, and ultrapure water from the Milli-Q® Type 1 Ultrapure Water System was added to a final volume of 50 ml. The solution was vortexed, allowed to stand for 3 minutes, and then sterilized by filtration using a 0.22 μm sterile membrane filter (Merck Millex™-GS Sterile Syringe Filter Unit, MCE). A 200 mM glucose stock solution was prepared and aliquoted into 2 ml EP tubes for storage.

## Preparation of Biomarker Induction Solutions at Different Concentrations

#### 1.Uric Acid

| 2 mM Uric Acid PBS Solution (µl) | No Uric Acid PBS (µl) | Final Concentration (µM) |
| --- | --- | --- |
| 100 | 0 | 100 |
| 95 | 5 | 10 |
| 90 | 10 | 20 |
| 75 | 25 | 50 |
| 50 | 50 | 100 |
| 0 | 100 | 200 |

#### 2.Lactic Acid

| Lactic Acid Solution (µl) | No Lactic Acid (µl) (ddH2O) | Final Concentration (µM) |
| --- | --- | --- |
| 100 | 0 | 625 |
| 94 | 6 | 1250 |
| 87.5 | 12.5 | 2500 |
| 75 | 25 | 5000 |
| 50 | 50 | 20000 |
| 0 | 100 | 40000 (40 mM) |

#### 3.Tryptophan

| 1 mM Tryptophan Solution (µl) | ddH2O (µl) | Final Concentration (µM) |
| --- | --- | --- |
| 100 | 0 | 0 |
| 95 | 5 | 0.5 |
| 90 | 10 | 1 |
| 75 | 25 | 2.5 |
| 50 | 50 | 5 |
| 0 | 100 | 10 |

#### 4.Glucose

| 200 mM Glucose Solution (µl) | ddH2O (µl) | Final Concentration (mM) |
| --- | --- | --- |
| 100 | 0 | 20 |
| 95 | 5 | 1 |
| 90 | 10 | 2 |
| 75 | 25 | 5 |
| 50 | 50 | 10 |
| 0 | 100 | 20 |

## Bacterial Transformation

1. Trans 5a Chemically Competent Cells (TransGene CD201-01) were retrieved from the -80°C freezer and allowed to thaw on ice for 10 minutes.
2. 10 ng of the target plasmid was added to the thawed competent cells, gently mixed, and incubated on ice for 30 minutes. During this period, a water bath was prepared and set to 42°C.
3. The competent cell-plasmid mixture was subjected to heat shock at 42°C for 45 seconds, followed by an immediate transfer to ice for 2 minutes without agitation of the centrifuge tube.
4. Subsequently, 500 µl of sterile LB medium (without antibiotics) was added to each tube, mixed thoroughly, and incubated at 37°C with shaking at 200 rpm for 1 hour to facilitate bacterial recovery. Meanwhile, LB plates with the appropriate antibiotic were prepared and placed in the 37°C incubator.
5. Transformed competent cells were plated onto LB agar plates containing the appropriate antibiotic and spread evenly. The plates were incubated at 37°C until the liquid was absorbed, then inverted and incubated overnight at 37°C.

## Bacterial Seed Culture Preparation, Induction, and Fluorescence Assay Testing

1. Add 3 ml of appropriate antibiotic LB into a 12 ml culture tube.
2. Pick 3 single colonies from the plate into the 3 ml LB with the appropriate antibiotic.
3. Place in a shaker at 220 rpm, 37°C, and incubate overnight.
4. Take 60 µl of the overnight culture and add it to 6 ml of LB with the appropriate antibiotic, and incubate at 220 rpm until the OD reaches 0.2-0.3, allowing the culture to reach the log phase.
5. Mix the log-phase culture well using a pipette, and transfer 900 µl into a 96-well deep-well plate.
6. Add 100 µl of the prepared biomarker inducer (uric acid/lactic acid/glucose/tryptophan) into the 900 µl log-phase culture.
7. Place the deep-well plate into a shaker at 1000 rpm, 37°C, and incubate overnight.
8. Transfer 150 µl of the overnight culture from the deep-well plate into a black microplate.
9. Place it into a TECAN Spark to measure the corresponding OD and fluorescence values.

## Fluorescent Protein Standard Expression and Purification

1. Pick 3 single colonies from the plate into 50 ml of LB with the appropriate antibiotic.
2. Place in a shaker at 220 rpm, 37°C, and incubate overnight.
3. Centrifuge at 10000 rpm for 5 minutes and discard the supernatant.
4. Add 8 ml of lysis buffer, and mix thoroughly.
5. Place in an ultrasonic cell disruptor for 5 minutes:
   * Power = 600W
   * Sonication on-time: 5s
   * Sonication off-time: 5s
6. Centrifuge and collect the supernatant.
   * The subsequent protein purification will use the Beyotime His-tag Protein Purification Kit (Denaturant-resistant): P2229S for purification.
   * Load BeyoGold™ His-tag Purification Resin (denaturant-resistant) into the column, equilibrate the purification column twice with 1 ml lysis buffer.
   * Load the supernatant onto the column. To improve yield, collect the flow-through and reload it 3-5 times to fully bind the His-tag protein.
   * Wash the column 5 times with 1 ml of denaturing lysis buffer.
   * Wash the column 5 times with 1 ml of washing buffer.
   * Elute the protein 6-10 times with 0.5 ml of elution buffer, collecting the final elution.

## SDS-PAGE

1. Take 20 µl of each sample and add 5 µl of SDS-PAGE protein loading buffer (Boyetime P0015), vortex to mix, and incubate at 98°C in a water bath for 10 minutes to prepare loading samples.
2. Centrifuge at 10000 rpm for 1 minute.
3. Prepare 1X MOPS-SDS running buffer by diluting 50 ml of 20X MOPS-SDS running buffer in 950 ml of ddH2O.
4. Set up YoungPAGE™ (Genscript: M00928) pre-cast gels in a Biorad Mini-PROTEAN Tetra Vertical Electrophoresis Cell according to the manufacturer's instructions, and add the 1X MOPS-SDS running buffer.
5. Load 10 µl of the prepared samples into the gel wells, and add Broad Multi Color Pre-Stained Protein Standard (Genscript: M00624) as the protein molecular weight marker into any empty wells.
6. Run the electrophoresis at 200 mV for 20 minutes, adjusting the electrophoresis time based on the actual results.
7. After electrophoresis, remove the SDS gel and proceed with staining and destaining according to the manufacturer's instructions.
8. After destaining, place the gel in a ChemiDoc Imaging System (Biorad: 12003153) for gel imaging and upload the images.

![](https://static.igem.wiki/teams/5460/engineering/glimpse.png)

*A Glimpse of our wet-lab daily work*

{% endblock %}

{% extends "layout.html" %}
{% block extraStyles %}
{% endblock %}
{% block title %}Results{% endblock %}
{% block lead %}You can describe the results of your project and your future plans here.{% endblock %}
{% block page\_content %}

# Results

## **Achieving Engineering Success of a Versatile Biomarker Detection System**

Our iGEM project successfully developed a **modular detection system for various biomarkers**, innovatively using two plasmid modules: the Auxiliary (A) system and the Reporter assay (R) module. The A module facilitates the entry of non-permeable biomarkers into *E. coli* with auxiliary proteins, while the R module quantifies their effects using fluorescent proteins.

Our **validation confirmed the system's effectiveness** across **multiple biomarkers**, demonstrating a versatile platform suitable for expanding biomarker detection capabilities in synthetic biology.

Overview

Module I: Uric Acid

Module II: Glucose

Module III: Lactic Acid

Module IV: Tryptophan

Fluorescence Crosstalk

## Overview

Many outstanding iGEM teams in the past have developed various detection systems to identify a range of biomarkers, which can include environmental heavy metals, proteins related to human health, or small molecules linked to food safety. The mechanisms of action generally fall into two categories:

1. **Direct mechanism:** The biomarker can directly cross the cell membrane and act on a specific sensitive promoter, thereby starting or stopping the expression of downstream proteins.
2. **Indirect mechanism:** The biomarker cannot directly cross the membrane or may need to interact with a protein first, forming a complex that then activates or terminates downstream expression.

We summarized the designs of these teams and developed our own system. In our system, we designed two plasmids, dividing them into two types and abstracting two functional modules:

1. **A (Auxiliary system) module:**
   1. This module is primarily responsible for encoding auxiliary proteins, such as transport proteins and intermediate proteins. Since some biomarkers cannot directly cross the cell membrane, transport proteins are required to help these biomarkers enter the *E. coli* cells.
   2. Additionally, these biomarkers may need to first form a complex with certain proteins before they can act on the sensitive promoter and initiate downstream gene expression.
   3. To achieve these functions, we used a strong promoter to express auxiliary proteins, while also designing standard Golden Gate interfaces to allow future replacement of auxiliary proteins, enhancing the flexibility and adaptability of the module.
2. **R (Reporter assay) module:**
   1. This module is mainly responsible for generating the signal, typically achieved by encoding a fluorescent protein downstream of a sensitive promoter. By detecting the fluorescent signal, the impact of the biomarker on the bacteria can be evaluated.
   2. Standard Golden Gate interfaces were also designed upstream and downstream of the sensitive promoter, facilitating other research teams in constructing their own sensitive promoters, thus promoting further research.

We conducted the following engineering validations based on this design:

1. To validate the performance of the system, we tested it against three different biomarkers to ensure their feasibility and effectiveness under preset conditions.
2. To enable simultaneous detection, we loaded three different fluorescent proteins (mTagBF2, sfGFP, and mKate2) into the R modules for the three biomarkers, and verified that their signals could be accurately detected.
3. To ensure that there was no crosstalk between these three fluorescent proteins in the designed hardware system, we designed and conducted a fluorescence crosstalk experiment, specifically testing their crosstalk at different excitation-emission wavelengths.
4. To enhance the system's flexibility, we designed a standard interface and, through modular assembly via this interface, successfully validated the ability to test a fourth biomarker, further expanding the system’s range of applications.

## Module I: Uric Acid

We cultured the strains harboring the uric acid-responsive plasmids on LB agar plates corresponding to their resistance, and after obtaining monoclonal growth, we selected three monoclonal bacterial strains. These were then inoculated into LB broth with the appropriate resistance and cultured overnight. On the following day, the cultures were diluted into the logarithmic growth phase and supplemented with PBS solutions containing varying concentrations of uric acid, after which the cultures were further incubated for 16 hours. Post incubation, 150 μL of the culture medium was transferred to a TECAN plate reader to measure the fluorescence signal of mTagBF2 as well as the optical density (OD). The experimental results indicated that as the concentration of uric acid increased, the relative fluorescence values of the strains also increased, suggesting that the presence of uric acid can activate the expression of fluorescent proteins within the system. To more accurately obtain the response dynamics parameters of the system, we performed a Hill reaction fitting on the response curve.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-1.jpg)

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).

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-2.jpg)

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.

## Module II: Glucose

Strains containing the glucose-responsive element were cultured on SOB agar plates corresponding to their resistance, and monoclonal colonies were selected. Three of these monoclonal colonies were then inoculated into M9 medium supplemented with the appropriate resistance and incubated overnight. On the subsequent day, the cultures were diluted to the logarithmic growth phase and treated with glucose solutions of varying concentrations, followed by a further 16 hours of incubation. Post incubation, 150 μL of the culture medium was transferred to a TECAN plate reader to measure the fluorescence signal of mKate2 as well as the optical density (OD). The experimental results indicated that as the concentration of glucose increased, the relative fluorescence values of the strains also increased, demonstrating that the presence of glucose can activate the expression of fluorescent proteins within the system. To more accurately determine the system's response dynamics parameters, a Hill reaction fitting was applied to the response curve.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-3.jpg)

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

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-4.jpg)

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.

## Module III: Lactic Acid

Bacterial strains equipped with a lactate-responsive element were cultured on LB agar plates with appropriate antibiotics, yielding monoclonal colonies. Three such colonies were selected and inoculated into LB medium with corresponding antibiotics for overnight cultivation. On the subsequent day, the cultures were diluted to the logarithmic growth phase and exposed to lactate solutions of varying concentrations for a further 16 hours of incubation. After the incubation period, 150 μL aliquots of the culture medium were assessed for mKate2 fluorescence and optical density (OD) using a TECAN plate reader. The experimental data revealed a correlation between increased lactate concentration and elevated relative fluorescence values, suggesting that lactate presence triggers the expression of fluorescent proteins within the system. To accurately determine the kinetic parameters of the system's response, the response curves were subjected to Hill equation fitting.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-5.jpg)

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).

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-5.jpg)

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 IV: Tryptophan

Strains containing the tryptophan-responsive element were streaked on LB agar plates with corresponding antibiotic resistance to obtain monoclonal colonies. Three of these colonies were selected and inoculated into LB medium with the appropriate antibiotics for overnight culture. On the next day, the cultures were diluted to the logarithmic growth phase and supplemented with tryptophan solutions of varying concentrations for an additional 16 hours of incubation. After incubation, 150 μL of the culture medium was measured for mKate2 fluorescence and optical density (OD) using a TECAN plate reader. The results showed a decrease in relative fluorescence values with increasing tryptophan concentration, indicating that tryptophan represses the expression of fluorescent proteins in the system. To accurately determine the kinetic parameters of the system's response, the response curves were fitted with the Hill equation.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-7.jpg)

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).

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-8.jpg)

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.

## Fluorescence Crosstalk

The fluorescent proteins—mTagBF2, sfGFP, and mKate2—were cloned into the pUC57 kanamycin-resistant vector, with the same promoter and RBS utilized to regulate their expression. The plasmids were transformed into DH5α bacterial strains and monoclonal colonies were selected after cultivation on agar plates containing the appropriate antibiotics. These colonies were grown overnight in 5 mL of LB broth with corresponding antibiotics. Fluorescence measurements were then conducted using a TECAN Spark microplate reader, with the excitation spectra recorded for each protein at their specific wavelengths, resulting in the targeted protein fluorescing without interference from the others. These results demonstrate the absence of crosstalk between the different fluorescent proteins, providing a theoretical basis for the simultaneous triple-channel detection capability of our hardware.

![](https://static.igem.wiki/teams/5460/engineering/bj1-eng-9.jpg)

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 mTagBF2, the emission intensity of mTagBF2 is also markedly higher than the other two fluorescent proteins, with an emission intensity more than 20 times that of the others.
3. 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.

{% endblock %}