iGEM 2024 — Team squirrel-chn

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[Overview](#a1)

Overview

Our team conducted a three-month-long series of experiments. The detailed schedule is as follows:

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The detailed information of the experiments and the raw experimental data are as follows:**（Total 171 pages）**

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[Overview](#a1)
[Construction and Verification of the Fat-Reduction System](#a2)
[1.1 Construction of GLP-1 Production Chassis Strain](#b1)
[1.2 Design of DPP-4 Protein Inhibitor](#b2)
[Construction and Validation of Muscle-Building System](#a3)
[2.1 Construction of Bimagrumab Production Strain](#b3)
[Construction and Verification of the Suicide System](#a4)
[3.1 Verification of the Function of the Arabinose Operon](#b4)
[3.2 Low-Temperature Inducible Promoter Function Validation](#b5)
[3.3 Construction of the Suicide System](#b6)
[Reference](#a5)

Overview

Our team successfully developed a fat-reduction and muscle-enhancement system using Escherichia colias the chassis bacterium. Through codon optimization and genetic engineering, we designed engineered bacteria capable of secreting GLP-1 and Bimagrumab, aimed at reducing fat and increasing muscle mass. Additionally, we designed a DPP-4 inhibitor to extend the half-life of GLP-1, with its stability confirmed via molecular dynamics simulations. We also constructed arabinose operon-induced and cold-inducible suicide systems to prevent gene leakage from the engineered bacteria. The successful establishment of these systems lays the foundation for future applications of probiotics in fat reduction.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img1.png)

Figure 1. Engineering: Design-Build-Test-Learn cycle diagram

Construction and Verification of the Fat-Reduction System

1.1 Construction of GLP-1 Production Chassis Strain

**Design**

Glucagon-like peptide-1 (GLP-1) is a hormone primarily produced by intestinal L-cells, which promotes insulin secretion and inhibits gastric emptying to achieve weight loss[1]. E. coli has several advantages as a chassis for genetic engineering, including rapid growth, ease of cultivation, straightforward genetic manipulation, low cost, and precise genome modification. This makes it an ideal choice for producing proteins and other biomolecules[2]. Therefore, we selected E. coli as the microbial chassis for GLP-1 production.

**Build**

First, we performed codon optimization of the GLP-1 sequence based on E. coli and commissioned Jinweizhi Company for fragment synthesis, including the synthesis of fragment primers. We chose BBa\_J23100 as the promoter, BBa\_B0034 as the ribosome binding site, and BBa\_B0015 as the terminator codon (Fig 2). Since our goal is to develop a probiotic capable of continuously secreting GLP-1, we attached an extracellular signal peptide, PelB, to the N-terminus of GLP-1 to facilitate the successful secretion of GLP-1 by the engineered bacteria.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img2.png)

Figure 2. (A) Gene Circuit Diagrams of the Fat-Reduction and Muscle-Building Systems (B) Plasmid Map

Next, we amplified the PelB-GLP-1 fragment using PCR (Fig 3.A) and ultimately integrated this fragment into the vector pET23b. The construct was then transformed into E. coli BL21 for protein expression verification. The transformed engineered bacteria were plated on LB agar containing ampicillin, resulting in the successful selection of monoclonal colonies (Fig 3.B).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img3.png)

Figure 3. Construction of Engineered Strains (A) PCR Product Validation (B) Monoclonal Colonies Containing the PelB-GLP-1 Fragment

**Test**

To determine whether the engineered bacteria could successfully express GLP-1 and secrete it extracellularly with the assistance of PelB, we centrifuged the culture containing the PelB-GLP-1 fragment to obtain the supernatant and bacterial pellet. The proteins were extracted from the bacterial pellet using a lysis method. We then used an ELISA kit (Shanghai Enzyme Linked, YJ022729) to verify the expression of GLP-1 in the engineered strain. The results indicated that in the absence of the PelB signal peptide, the intracellular concentration of GLP-1 reached 60.7 pg/mL, while the extracellular concentration was only 8 pg/mL. However, with the assistance of the PelB signal peptide, the extracellular level of GLP-1 increased to 62 pg/mL (Fig 4.B). Subsequently, we performed Western blotting on the extracted GLP-1 from both intracellular and extracellular sources. The results showed that without the PelB signal peptide, GLP-1 was predominantly localized intracellularly; whereas, after linking the PelB signal peptide to the N-terminus of GLP-1, the extracellular concentration significantly increased (Fig 4.A). Finally, we validated that GLP-1 and the PelB-GLP-1 fragment did not adversely affect the growth of the strains (Fig 4.C).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img4.png)

Figure 4. Validation of GLP-1 and Bimagrumab Expression (A) WWestern Blotting Verification of Intracellular and Extracellular Expression of GLP-1 and PelB-GLP-1 (B) ELISA Verification of Intracellular and Extracellular Expression of GLP-1 and PelB-GLP-1 (C) Western Blotting Verification of Intracellular and Extracellular Expression of Bimagrumab and PelB-Bimagrumab

**Learn**

We successfully constructed engineered bacteria capable of secreting GLP-1 extracellularly. However, GLP-1 is prone to degradation by dipeptidyl peptidase-4 (DPP-4) in the bloodstream, resulting in a half-life (t₁/₂) of only a few minutes. Therefore, our team chose to design a protein inhibitor of DPP-4 to enhance the half-life of GLP-1 in vivo, allowing it to function more effectively.

1.2 Design of DPP-4 Protein Inhibitor

**Design**

As there are no naturally occurring protein inhibitors that can competitively bind to DPP-4, we plan to utilize existing deep learning models for de novo design. We employed four deep learning models: AlphaFold3[3], RFdiffusion[4], ProteinMPNN[5], and ESMfold[6].

**Build**

To design a protein inhibitor for DPP-4, we established a comprehensive design and screening workflow: RFdiffusion generates the protein backbone, ProteinMPNN generates the side chains, and ESMfold filters for high-quality sequences. First, we modeled the protein complex of GLP-1 and DPP-4 using AlphaFold, then selected the key residues E167, E168, and Y624 in the DPP-4 pocket as hotspots using PyMOL. Next, we utilized the Practical Considerations for Binder Design module for the design. Subsequently, the selected backbone was used in the ProteinMPNN's FastRelax protocol for side chain generation. Finally, the complete amino acid sequences generated were modeled using ESMfold (Fig 5).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img5.png)

Figure 5. Technical Route for DPP-4 Protein Inhibitor

**Test**

For each step of the generated sequences, we established screening criteria:

Backbone Generation: We used structural observation methods to empirically select the most suitable structure for subsequent side chain generation.

Side Chain Generation: After generating the complete amino acid sequence, we modeled it using ESMfold and filtered for sequences with RMSD < 15 and pLDDT > 80 compared to the Cα of the protein backbone, ultimately selecting 18 sequences suitable for molecular dynamics simulation (Fig 6.A).

Molecular Dynamics Simulation: The selected sequences underwent long-term molecular dynamics simulations using the Amber program. This included a 100 ns equilibration followed by 100 ns of production dynamics, totaling 200 ns.

MM/PBSA Calculation: We performed MM/PBSA calculations on the molecular dynamics results, identifying one sequence with a binding energy to DPP-4 that was superior to that of GLP-1. The energy of the DPP-4 protein inhibitor (DPP-4PI) in solution was -6522.0487 kcal/mol, compared to -6474.9481 kcal/mol for GLP-1 with DPP-4 (Fig 6.B).

Trajectory Analysis: Finally, we analyzed the trajectories from the molecular dynamics simulations, confirming that both RMSD and RMSF trajectories remained within reasonable ranges (Fig 6.C and D).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img6.png)

Figure 6. Evaluation of DPP-4 Protein Inhibitor (A) Comparison of RMSD and pLDDT after modeling the artificially generated sequences. (B) Affinity analysis of GLP-1 and DPP-4 protein inhibitor with DPP-4. (C) RMSD of the DPP-4 protein inhibitor during molecular dynamics simulation. (D) RMSF of the DPP-4 protein inhibitor during molecular dynamics simulation.

**Learn**

We successfully established a workflow for artificially designed proteins and utilized existing deep learning models to effectively create a DPP-4 protein inhibitor, validating its rationality on a computational level. Although we have demonstrated the feasibility of this method computationally, we have not yet conducted experimental validation due to time constraints, which will be a core focus for our team in the future.

Construction and Validation of Muscle-Building System

2.1 Construction of Bimagrumab Production Strain

**Design**

Bimagrumab is a humanized monoclonal antibody that binds to type II hormone receptors on muscle cells. It is primarily used to increase skeletal muscle density for the treatment of muscle atrophy diseases and has been evaluated in several clinical trials. Therefore, we chose to utilize Bimagrumab to construct a muscle-building system for probiotic development.

**Build**

We first retrieved the sequence information for Bimagrumab from RCSB (PDB ID: 5NH3) and subsequently performed codon optimization. The optimized sequence and the designed primers were then synthesized by Genewiz. We selected BBa\_J23100 as the promoter, BBa\_B0034 as the ribosome binding site, and BBa\_B0015 as the stop codon (Fig 7). Similar to the scenario with GLP-1, Bimagrumab also needs to be continuously secreted into the extracellular space, so we attached the PelB signal peptide to the N-terminus of Bimagrumab.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img7.png)

Figure 7. Bimagrumab Gene Circuit Diagram

Next, we ligated the amplified sequence (Fig 8) into the vector pET23b and transformed it into E. coli  BL21 for protein expression validation.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img8.png)

Figure 8. Agarose Gel Electrophoresis Validation of Sequence Amplification

**Test**

We conducted Western blotting experiments to validate the expression of Bimagrumab and PelB-Bimagrumab both intracellularly and extracellularly. The results indicated that, in the absence of the PelB signal peptide, Bimagrumab was primarily located within the cells. However, after the addition of the PelB signal peptide to the N-terminus of Bimagrumab, its concentration in the extracellular space significantly increased(Fig 9).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img9.png)

Figure 9. Western blotting experiment demonstrates that Bimagrumab can be effectively secreted extracellularly with the help of the signal peptide PelB.

Finally, we verified that the plasmids containing the Bimagrumab and PelB-Bimagrumab fragments did not affect the growth of the bacterial strain(Fig 10).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img10.png)

Figure 10. Effects of Bimagrumab fragment on the growth condition of BL21

**Learn**

We successfully constructed the muscle-building system and verified that Bimagrumab can be effectively secreted into the extracellular space with the help of the PelB signal peptide. This indicates that our probiotic can reduce muscle loss while promoting fat reduction, helping patients maintain their health more effectively.

Construction and Verification of the Suicide System

3.1 Verification of the Function of the Arabinose Operon

**Design**

To ensure that the genetic fragments do not leak into the environment and to allow for the timely removal of the engineered bacteria from the patient's body when needed, we used the arabinose operon as a switch to induce bacterial death. Our team first verified the function of the existing part BBa\_I13453 in E. coli  DH5α.

**Build**

To verify whether the arabinose operon can function properly in E. coli DH5α, we linked the mRFP fragment to the arabinose operon (Fig 11) and assessed the operon's functionality by measuring fluorescence values at different arabinose concentrations.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img11.png)

Figure 11. Arabinose Operon Verification Gene Circuit Diagram

**Test**

We measured the expression of red fluorescent protein under the induction of the arabinose operon. As the concentration of arabinose increased, the Fluorescence/OD600 ratio also increased, demonstrating that the arabinose operon can induce the expression of the corresponding protein in response to arabinose (Fig 12).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img12.png)

Figure 12. Different Concentrations of Arabinose Operon Inducing the Expression of Red Fluorescent Protein's Fluorescence/OD600

**Learn**

We successfully tested the function of the arabinose operon in E. coli DH5α, which lays the foundation for the subsequent construction of the suicide system.

3.2 Low-Temperature Inducible Promoter Function Validation

**Design**

Under typical circumstances, if the engineered bacteria leak into the environment, they cannot self-destruct via arabinose induction. Therefore, we chose a more widely occurring environmental factor—low temperature—as the condition to induce the engineered bacteria to self-destruct. In the parts library, we identified the low-temperature inducible promoter pCspA (BBa\_K4987003) and began by verifying its functionality.

**Build**

Using a method similar to that used to validate the arabinose operon, we connected the mRFP segment to the low-temperature inducible promoter pCspA (Fig 13). We then validated the function of pCspA by measuring the fluorescence of E. coli at different cultivation temperatures.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img13.png)

Figure 13. Low-Temperature Inducible Promoter Validation Gene Circuit

**Test**

The results indicated that under low-temperature conditions, the OD600 value of BL21 was only 0.5 within 12 hours, while at 37°C, the OD600 value reached 2.3. However, under low-temperature conditions, the Fluorescence/OD600 value reached 278, significantly higher than the Fluorescence/OD600 value at 37°C (Fig 14). This demonstrates that the low-temperature inducible promoter CspA can effectively induce the expression of red fluorescent protein under low-temperature conditions.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img14.png)

Figure 14. Expression of Red Fluorescent Protein Induced by Low-Temperature Inducible Promoter pCspA

**Learn**

We successfully validated the functionality of the low-temperature inducible promoter pCspA in E. coli DH5α, laying the groundwork for the subsequent construction of the suicide system.

3.3 Construction of the Suicide System

Design

To successfully induce the suicide of engineered bacteria, we selected two lytic proteins derived from the T4 bacteriophage: T4 Holin and T4 Lysozyme. T4 Holin is a small membrane protein with two transmembrane domains, with the N-terminus and C-terminus located in the cytoplasm and extracellular space, respectively. T4 Lysozyme is a small globular protein that can break the chemical bonds between N-acetylglucosamine and N-acetylmuramic acid. The combined action of these two proteins can effectively kill the engineered bacteria.

**Build**

We first connected the T4 Holin and T4 Lysozyme fragments to the arabinose operon and low-temperature inducible promoter, respectively, and then linked the two systems using an 'OR' gate (Fig 15).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img15.png)

Figure 15. Suicide System Gene Circuit Diagram

**Test**

We verified the survival status of bacteria under the induction of the two promoters. The results showed that in the presence of 0.5 mM/L arabinose, the OD600 of the culture significantly decreased after 5 hours of incubation, approaching 0 by the 20th hour (Fig 16.A). In contrast, under low-temperature conditions at 16°C, the engineered bacteria containing the lysozyme sequence remained at a low density, with the OD600 nearly constant at 0.3 (Fig 16.B).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img16.png)

Figure 16. Suicide System Verification (A) Growth of E. coli  DH5α under 0.5 mM/L arabinose (B) Growth of E. coli  DH5α at 16°C

**Learn**

Our team successfully constructed a suicide system using pBAD and pCspA as promoters and T4 Holin and T4 Lysozyme sequences as suicide genes, and we successfully verified the functionality in E. coli  DH5α.

Reference

[1] Li Y, Hansotia T, Yusta B, et al. Glucagon-like peptide-1 receptor signaling modulates β cell apoptosis[J]. Journal of Biological Chemistry, 2003, 278(1): 471-478.

[2] Lee S Y. High cell-density culture of E. coli [J]. Trends in biotechnology, 1996, 14(3): 98-105.

[3] Abramson J, Adler J, Dunger J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3[J]. Nature, 2024: 1-3.

[4] Watson J L, Juergens D, Bennett N R, et al. De novo design of protein structure and function with RFdiffusion[J]. Nature, 2023, 620(7976): 1089-1100.

[5] Dauparas J, Anishchenko I, Bennett N, et al. Robust deep learning–based protein sequence design using ProteinMPNN[J]. Science, 2022, 378(6615): 49-56.

[6] Song Y, Yuan Q, Chen S, et al. Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures[J]. Nature Communications, 2024, 15(1): 8180.

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We are Squirrel-CHN, and this document outlines the detailed experimental protocols for our project, designed to ensure reproducibility for future teams. Our work includes constructing engineered strains, covering all steps from sequence design to plasmid extraction and PCR amplification. We also employ techniques such as gel recovery, restriction digest, ligation, and transformation. For protein expression verification, we use fluorescence microscopy, ELISA, SDS-PAGE, and Western blotting. Additionally, we integrate artificial protein reengineering with tools like AlphaFold and molecular dynamics simulations to refine and analyze our target proteins. By providing these comprehensive procedures, we aim to make it easier for future teams to replicate and build upon our work.

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[Overview](#a1)
[Construction of Engineered Strains](#a2)
[GLP-1 and Bimagrumab Expression Verification](#a3)
[Artificial De Novo Generation of DPP-4 Protein Inhibitors](#a4)
[Construction and Validation of the Suicide System](#a5)
[Testing of the Arabinose Operon](#a6)
[Testing of the Low-Temperature Inducible Promoter](#a7)
[Testing of the Suicide System](#a8)
[Construction and Characterization of Engineered EcN](#a9)
[Summary：](#a10)
[Reference：](#a11)

Overview

First, our team successfully constructed and validated the expression systems for GLP-1 and Bimagrumab, detecting the expression of both proteins extracellularly. Secondly, we designed DPP-4 protein inhibitors at the computational level and verified their affinity to DPP-4 through molecular dynamics simulations, showing superiority over GLP-1. Then, we successfully built an arabinose operon and a low-temperature induced promoter to create a cell suicide system, confirming its functionality. Finally, using pSB1A3 as a vector, we successfully transformed the constructed fat-reduction system, muscle-gaining system, and suicide system into Escherichia coli Nissle 1917. Additionally, we simulated the intestinal environment and conducted characterization analysis on EcN, demonstrating its effective survival in the simulated intestinal conditions.

Construction of Engineered Strains

To obtain engineered strains capable of effectively secreting GLP-1 and Bimagrumab, we designed a genetic circuit containing the sequences for GLP-1 and Bimagrumab (Fig 1). We selected E. coli  BL21 as the chassis microorganism for constructing the engineered strains and utilized genetic engineering techniques to clone GLP-1, PelB-GLP-1, Bimagrumab, PelB-Bimagrumab, and PelB-GLP-1-Bimagrumab into E. coli BL21.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img1.png)

**Figure 1.** Genetic Circuit Diagram of GLP-1 and Bimagrumab

Firstly, we obtained the sequences for GLP-1, Bimagrumab, and the secretion signal peptide PelB from NCBI and optimized the codons for E. coli. We then designed PCR primer sequences (Fig 2.A) and had a company synthesize the coding sequences and primer sequences for GLP-1, PelB-GLP-1, Bimagrumab, and PelB-Bimagrumab. We amplified the gene sequences of GLP-1, PelB-GLP-1, Bimagrumab, and PelB-Bimagrumab through PCR. Using overlap PCR, we integrated the PelB-GLP-1 fragment and the Bimagrumab fragment to obtain the PelB-GLP-1-Bimagrumab fragment (Fig 2.B). Subsequently, we cloned the five fragments into the pET23b plasmid via NdeI and XhoI restriction sites. Finally, the recombinant plasmids were transformed into E. coli BL21, and we plated them on LB solid media containing ampicillin, ultimately obtaining single colonies (Fig 2.C).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img2.png)

**Figure 2.** Construction of Engineered Strains (A) Sequences and primer design for PelB, GLP-1, and Bimagrumab (B) Verification of PCR products (C) Single colonies containing the PelB-GLP-1-Bimagrumab fragment

The results indicate that we successfully amplified the fragments of GLP-1, PelB-GLP-1, Bimagrumab, PelB-Bimagrumab, and PelB-GLP-1-Bimagrumab, and cloned them into the vector pET23b. Ultimately, we obtained single colonies on LB solid media containing ampicillin.

GLP-1 and Bimagrumab Expression Verification

To verify the expression of GLP-1 and Bimagrumab, as well as the functionality of PelB, we measured the concentrations of GLP-1 and Bimagrumab in both intracellular and extracellular environments using ELISA and Western blotting. After centrifuging cultures containing GLP-1, PelB-GLP-1, Bimagrumab, and PelB-Bimagrumab, we obtained supernatants and bacterial pellets, extracting proteins from the pellets using lysis methods. Using an ELISA kit (Shanghai Enzyme Linked, YJ022729), we confirmed GLP-1 expression in the engineered strains. Results showed that without the secretion signal peptide PelB, the intracellular concentration of GLP-1 reached 61.26 pg/mL, while the extracellular concentration was only 9.19 pg/mL; with PelB assistance, extracellular GLP-1 levels increased to 63.81 pg/mL (Fig 3.B). Western blotting of extracted GLP-1 and Bimagrumab confirmed that, without PelB, both proteins were predominantly intracellular. However, after attaching PelB to the N-terminus of GLP-1 and Bimagrumab, their extracellular concentrations significantly increased (Fig 3.A and C). Finally, we verified that GLP-1, PelB-GLP-1, Bimagrumab, and PelB-Bimagrumab fragments did not affect the growth of the strains (Fig 3.D).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img3.png)

**Figure 3.** Verification of GLP-1 and Bimagrumab Expression (A) Western blotting validation of intracellular and extracellular expression of GLP-1 and PelB-GLP-1 (B) ELISA validation of intracellular and extracellular expression of GLP-1 and PelB-GLP-1 (C) Western blotting validation of intracellular and extracellular expression of Bimagrumab and PelB-Bimagrumab (D) Effects of different fragments on the growth condition of BL21

The experimental results indicate that the engineered strains successfully expressed GLP-1 and Bimagrumab, with significantly increased extracellular levels of both proteins facilitated by the secretion signal peptide PelB. Additionally, we demonstrated that the genetic engineering did not have a significant impact on the growth of BL21.

Artificial De Novo Generation of DPP-4 Protein Inhibitors

Since there are no naturally occurring protein inhibitors that can competitively bind to DPP-4, our team chose to design a DPP-4 protein inhibitor from scratch using deep learning models. We established a comprehensive workflow based on advanced deep learning models: RFdiffusion for generating the protein backbone, ProteinMPNN for side-chain generation, ESMfold for screening high-quality sequences, Amber for molecular dynamics simulations, and MM/PBSA to compare the binding energies of GLP-1 and the inhibitor with DPP-4 (Fig 4.A). During the backbone generation with RFdiffusion, we utilized the Practical Considerations for Binder Design module, selecting critical residues E167, E168, and Y624 in the DPP-4 pocket as hotspots. We generated 200 sequences of random lengths (10-100 amino acids) and filtered for promising scaffolds. The selected scaffolds were subjected to side-chain generation using ProteinMPNN's FastRelax protocol, resulting in 18 sequences suitable for molecular dynamics simulations based on RMSD < 15 and pLDDT > 80 (Fig 4.B). We processed these sequences with AmberTools and conducted long-term molecular dynamics simulations using Amber, with a 100 ns equilibration and 100 ns of production (totaling 200 ns). MM/PBSA calculations yielded one sequence with a binding energy superior to that of GLP-1 with DPP-4: DPP-4PI had a binding energy of -6522.0487 kcal/mol, compared to GLP-1's -6474.9481 kcal/mol (Fig 4.C). Finally, we analyzed the molecular dynamics trajectories, confirming that RMSD and RMSF values remained within acceptable ranges (Fig 4.D and E). We selected a stable frame from the simulation for structural observation, revealing the binding mode between DPP-4 and its protein inhibitor (Fig 5).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img4.png)

**Figure 4.** Artificial De Novo Generation of DPP-4 Protein Inhibitors (A) Workflow of artificial protein generation techniques (B) Comparison of RMSD and pLDDT after modeling the generated sequences (C) Affinity analysis of GLP-1 and the DPP-4 protein inhibitor with DPP-4 (D) RMSD of molecular dynamics simulation for the DPP-4 protein inhibitor (E) RMSF of molecular dynamics simulation for the DPP-4 protein inhibitor

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img5.png)

**Figure 5.** Structure of DPP-4 (orange) and its Protein Inhibitor (blue)

The results indicate that, through the established workflow and utilizing existing deep learning models, we successfully designed a DPP-4 protein inhibitor and validated its feasibility at the computational level.

Construction and Validation of the Suicide System

To prevent the leakage of genetic material from the engineered strains, we connected the arabinose operon and low-temperature inducible promoter in an "OR" gate configuration, allowing the engineered strains to express lysozyme under specific conditions for self-destruction (Fig 6A). First, we surveyed the iGEM parts library and identified the arabinose operon pBAD (BBa\_I13453) and the low-temperature inducible promoter pCspA (BBa\_K4987003), which we then amplified (Fig 6B).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img6.png)

**Figure 6.** Construction of the Suicide System (A) Gene circuit diagram of the suicide system (B) Agarose gel electrophoresis validation of the fragments required for the suicide system

Testing of the Arabinose Operon

We measured the expression of red fluorescent protein induced by the arabinose operon (Fig 7.A). As the concentration of arabinose increased, the Fluorescence/OD600 values consistently rose, demonstrating that the arabinose operon can effectively induce the expression of the corresponding protein in response to arabinose (Fig 7.B) [3].

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img7.png)

**Figure 7.** Validation of the Arabinose Operon in the Suicide System (A) Gene circuit verification of the arabinose operon (B) Fluorescence/OD600 of red fluorescent protein expressed under different concentrations of arabinose induced by the arabinose operon

Testing of the Low-Temperature Inducible Promoter

First, we validated the functionality of the low-temperature inducible promoter. Our experiments demonstrated that the optimal growth temperature for E. coli  DH5α is 37°C, with growth slowing as the temperature decreases (Fig 8.B). We introduced the low-temperature inducible promoter pCspA and linked it to the red fluorescent protein to assess its function (Fig 8.A). The results showed that under low-temperature conditions, the OD600 value of DH5α reached only 0.5 within 12 hours, while at 37°C, it reached 2.3. However, the Fluorescence/OD600 value at low temperature reached 278, significantly higher than the value at 37°C (Fig 8.C). This indicates that the low-temperature inducible promoter CspA can effectively induce the expression of the red fluorescent protein under low-temperature conditions.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img8.png)

**Figure 8.** Validation of the Low-Temperature Inducible Promoter in the Suicide System (A) Gene circuit verification of the low-temperature inducible promoter (B) Effect of temperature on the growth of DH5α (C) Expression of red fluorescent protein induced by the low-temperature inducible promoter pCspA

Testing of the Suicide System

We obtained the sequences for two types of lysozymes, T4 Holin and T4 Lysozyme, from the T4 bacteriophage via the NCBI website and amplified them through PCR (Fig 6.B). Next, we genetically connected the arabinose operon pBAD and the low-temperature inducible promoter pCspA to the T4 Holin and T4 Lysozyme sequences. We then validated the survival status of the bacteria under the induction of both promoters. The results showed that in the presence of 0.5 mM/L arabinose, the OD600 of the culture significantly declined after 5 hours, approaching zero by the 20th hour (Fig 9.A). In contrast, under low-temperature conditions at 16°C, the engineered strains containing the lysozyme sequence maintained a low density, with the OD600 remaining around 0.3 (Fig 9.B).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img9.png)

**Figure 9.** Validation of the Suicide System (A) Growth of E. coli  DH5α in the presence of 0.5 mM/L arabinose (B) Growth of E. coli  DH5α at 16°C

In summary, our team successfully constructed a suicide system using pBAD and pCspA as promoters, with T4 Holin and T4 Lysozyme sequences as suicide genes, and effectively validated its functionality in E. coli  DH5α.

Construction and Characterization of Engineered EcN

Since our ultimate goal is to create a probiotic, we needed to select components suitable for E. coli  Nissle 1917 (EcN). By searching the iGEM parts library, we identified a suitable promoter (BBa\_J23100), ribosome binding site (BBa\_B0034), and terminator (BBa\_B0015) for EcN, and created a gene circuit diagram incorporating both the fat-reduction and muscle-building systems (Fig 10.A). Additionally, we chose the EcN-specific pSB1A3 as the vector and illustrated the plasmid map (Fig 10.B).

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img10.png)

**Figure 10.** (A) Gene circuit diagram of the fat-reduction and muscle-building systems

(B) Plasmid map suitable for EcN

We assessed the expression of GLP-1 in the engineered EcN. The ELISA results indicated that the engineered EcN successfully expressed GLP-1 and, with the help of PelB, secreted it extracellularly (Fig 11.A). Additionally, we evaluated whether the engineered strain affected the normal growth of EcN. The results showed that during the first 20 hours, the OD600 of the engineered EcN grew at a slower rate compared to the wild type, but after 20 hours, the OD600 of the engineered strain approached that of the wild type (Fig 11.B).

Since our engineered strain needs to function in the gut, we simulated the intestinal microenvironment to test its stability. First, we evaluated the antioxidant capacity of the strain using the DPPH scavenging assay (DPPH radical scavenging capacity kit, A153-1-1, Nanjing Jianchen). Next, we conducted acid resistance tests by resuspending the strains at different pH levels and measuring OD600. Finally, we assessed the bile salt tolerance by adding equal amounts of bacteria to LB media containing different concentrations of bile salts (0.0%–0.5%) and incubating at 37°C for 4 hours, followed by OD600 measurement. The results showed that the antioxidant capacity (Fig 11.C), pH tolerance (Fig 11.D), and bile salt tolerance (Fig 11.E) of the engineered strain did not significantly differ from those of the wild-type EcN.

![](https://static.igem.wiki/teams/5083/{{self.title()|lower}}/img11.png)

**Figure 11.** GLP-1 Expression in EcN and Physicochemical Properties (A) Extracellular expression of GLP-1 in engineered EcN (B) Growth comparison of engineered EcN and wild-type over 20 hours (C) Antioxidant capacity of engineered EcN (D) Acid tolerance of engineered EcN (E) Bile salt tolerance of engineered EcN

In summary, our team successfully integrated the fat-reduction system, muscle-building system, and suicide system into the pSB1A3 vector and transformed it into EcN. Furthermore, we validated the extracellular expression of GLP-1 and the growth characteristics of the engineered EcN. Finally, we simulated the intestinal microenvironment and demonstrated that the engineered EcN maintained comparable antioxidant capacity, acid tolerance, and bile salt tolerance to the wild type, indicating its potential to survive in the gut.

Summary：

Our team successfully constructed and validated expression systems for GLP-1 and Bimagrumab, designed a high-affinity DPP-4 inhibitor, and established an effective cell suicide system. By transforming the fat-reduction, muscle-building, and suicide systems into E. coli  Nissle 1917, we confirmed the engineered strain's survival in a simulated intestinal environment, ensuring its efficacy and safety. Additionally, the DPP-4 inhibitor designed using deep learning models demonstrated superior binding energy compared to GLP-1, highlighting the rationality and promise of this design. Experimental results indicate that the engineered strain can efficiently express and secrete target proteins while maintaining growth, showcasing its potential applications in medicine and biotechnology, thus providing a solid foundation for future research.

Reference：

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[3] Hecht A, Endy D, Salit M, et al. When wavelengths collide: bias in cell abundance measurements due to expressed fluorescent proteins[J]. ACS synthetic biology, 2016, 5(9): 1024-1027.

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