Engineering
Overview
Our project is centered around the synthetic biology "Design-Build-Test-Learn" (DBTL) cycle strategy, through which we developed a novel Syn-M cell therapy system via multiple rounds of iteration. The entire engineering process was divided into several key cycles, encompassing the full pipeline from target screening and receptor engineering to the integration of therapeutic functional modules. This fully embodies an engineering mindset featuring modular design, theoretical construction, closed-loop testing, and continuous optimization. Through this sustained, interlocking feedback loop, we progressively refined the outcomes of each cycle and continuously improved our cell therapy system based on empirical results.
Figure1. iGEM Engineering Cycle
Cycle-1
Design Concept of Engineered Macrophages for Liver Cancer Therapy
| Design | Hepatocellular carcinoma (HCC) is the sixth most common cancer globally and ranks among the top three in mortality, presenting a severe challenge for prevention and treatment. While CAR-T therapy has shown significant efficacy in hematological malignancies, its application in solid tumors like HCC is limited due to poor tumor infiltration. Our goal is to design a more effective immune cell therapy. Given that macrophages are the most abundant immune cells infiltrating the tumor microenvironment (TME), we plan to engineer a CAR-M to address the limitations of CAR-T therapy. |
| Build | Leveraging the characteristic high and specific expression of GPC3 in HCC, we designed a CAR construct linking GPC3 to CD3ζ. We hope to deliver the GPC3-CD3ζ CAR into induced pluripotent stem cells (iPSCs) using a lentiviral vector and subsequently differentiate them into mature macrophages. This approach aims to polarize the macrophages towards an M1 pro-inflammatory phenotype, enhancing their tumor-killing activity (Figure 2). |
| Test | Through discussions with our Principal Investigator (PI) and experts [Human Practices], we recognized that while CAR-M offers unique advantages in antigen presentation and infiltration capabilities, it still faces challenges such as tumor antigen heterogeneity and the tumor microenvironment. |
| Learn | Based on the limitations identified for CAR-M, our goal for the next iteration is to design a new macrophage strategy targeting tumor antigen heterogeneity and the immunosuppressive TME. |
Figure 2. GPC3-CD3ζ-CAR-M Design(Image source: Nat Immunol 2024;25:102–116)
| Design | Based on the first iteration results, we identified two key challenges: tumor antigen heterogeneity and the immunosuppressive TME. To address heterogeneity, we recognized the potential of switching from HCC-specific activation to liver microenvironment-specific activation. To counteract the immunosuppressive TME, we can modulate downstream gene expression post-activation. |
| Build | To realize this design concept, we needed a suitable foundational system to host these two components. Through literature review and discussions with our PI [Human Practices], we introduced the SynNotch system. We aim to use liver-specific (rather than HCC-specific) activation (Input) to address antigen heterogeneity, and subsequently maintain the anti-cancer M1 polarized phenotype (Output) to tackle the immunosuppressive TME. |
| Test | The SynNotch system consists of an extracellular antigen recognition domain, a Notch transmembrane domain, and an intracellular transcription factor (e.g., Gal4-VP64) (Figure 3). Upon recognition of a primary target antigen, it releases the transcription factor into the nucleus, initiating the expression of user-defined downstream genes. |
| Learn | This system allows for the incorporation of desired extracellular antibodies while offering programmable output with complex logic gates. This indicated that the SynNotch system is a crucial tool for achieving liver-specific activation and intracellular gene regulation in macrophages. |
Figure 3. Introduction to the SynNotch system (A) and our overall design concept (B).
Image adapted from: Cell. 2016 Feb; Cell, 164(4):780-791.
Image adapted from: Cell. 2016 Feb; Cell, 164(4):780-791.
Cycle-2
INPUT Module-Achieving Liver Microenvironment-Specific Activation
To achieve the preliminary input design, we needed to screen for a protein meeting the following criteria:
| Criteria | Rationale |
|---|---|
| Hepatocyte Localization | Hepatocytes are the primary cell type in the liver, essential for achieving liver-specific rather than HCC-specific activation. |
| High Specificity Expression | Ensures specificity and sensitivity of the input signal |
| Membrane Localization | Requirement of the SynNotch system |
The goal was to identify the target protein, obtain its single-chain variable fragment (scFv), and use it to construct the signal input part of the SynNotch receptor. Therefore, we conducted the following phases: (I) Screening of liver-specific membrane protein SLC17A2; (II) Preparation of Anti-SLC17A2 scFv; (III) Optimization and functional validation of the Syn-M based on Anti-SLC17A2 scFv.
(I) Screening of Liver-Specific Membrane Protein SLC17A2
| Design | Our target liver microenvironment-specific antigen needed to meet three criteria: high/ exclusive expression in the liver, a hepatocyte marker protein, and membrane localization. Accordingly, we utilized our team's self-developed Software for stepwise screening. |
| Build | First, we categorized all genes into five groups using the Protein Atlas to identify proteins highly expressed in the liver. These proteins were then filtered using our Software based on the criteria of hepatocyte-specific expression and cell membrane localization. |
| Test | Screening with the Protein Atlas yielded 978 liver-highly-expressed proteins (Figure 4). Inputting these into our Software for filtering ultimately identified SLC17A2 as the optimal candidate meeting the overlapping criteria (Figure 5).Software . |
| Learn | After identifying SLC17A2 with its high liver-specific expression, and considering our foundational SynNotch system, we needed to further investigate whether this protein has a transmembrane structure and possesses an ideal extracellular domain for antibody development. |
Figure 4. Distribution of All Genes Across Five Transcript Categories
Figure 5. Murine SLC17A2 Sequence(Resource from：
https://www.proteinatlas.org/humanproteome/tissue/liver)
https://www.proteinatlas.org/humanproteome/tissue/liver)
| Design | Based on the requirements from the first iteration, we needed to predict the transmembrane and extracellular domain structures of SLC17A2. |
| Build | For this purpose, we performed TMHMM-2.0 prediction for transmembrane helix structure and AlphaFold structure prediction for murine SLC17A2. Model |
| Test | TMHMM-2.0 prediction indicated that murine SLC17A2 has 10 transmembrane helices (Figure 6). AlphaFold structure prediction revealed it possesses an ideal extracellular structure, with amino acids 48-84 being relatively conserved, representing an ideal region for antibody development (Figure 7). |
| Learn | Combining software screening and structural predictions, we identified SLC17A2 as a suitable target for constructing the corresponding SynNotch extracellular domain to achieve liver microenvironment-specific activation. |
Figure 6. TMHMM-2.0 Prediction Results for Murine SLC17A2
Figure 7. AlphaFold 3D Structure Prediction of Murine SLC17A2
The red box highlights the extracellular region (amino acids 48-84).
The red box highlights the extracellular region (amino acids 48-84).
(II) Preparation of Anti-SLC17A2 scFv
| Design | Based on the prior screening phase identifying the liver-highly-specific target protein SLC17A2, we planned to prepare its single-chain variable fragment (scFv) as the extracellular domain of the SynNotch system to achieve liver-specific activation of macrophages. |
| Build | Initially, we planned to generate high-affinity scFvs against SLC17A2 using genetic engineering techniques. However, the process of scFv development involves multiple technical challenges requiring advanced experimental skills and extensive experience, which exceeded the technical capabilities of an undergraduate team within the project timeframe. To ensure antibody quality and project timeline, we decided to outsource the preliminary scFv preparation work to a professional biotechnology company. This company possesses mature scFv molecular construction and optimization platforms, enabling efficient production and optimization of antibodies, providing our project with high-affinity scFv materials meeting experimental expectations. |
| Test | We provided the 48-84 amino acid sequence to Xi'an Haina Biotechnology Co., Ltd. for the preparation of Anti-SLC17A2 scFv. The company delivered three antibody clones (No.1, No.2, No.3). We sequenced these antibodies and tested their efficacy (Figures 8, 9). Immunofluorescence results indicated that Clone No.3 performed significantly better than the other two. |
| Learn | Building upon the assistance of the professional biotechnology company, we further confirmed the optimal efficacy of Clone No.3 antibody, successfully obtaining the most suitable scFv for the SynNotch system extracellular domain. Parts |
Figure 8. Sequencing results of the three Anti-SLC17A2 scFv clones.
(The red parts are antigen complementary determining region (CDR).)
(The red parts are antigen complementary determining region (CDR).)
Figure 9. Immunofluorescence staining with Anti-SLC17A2 scFvs on mouse liver cancer cell line Hepa1-6 and mouse lung cancer cell line LLC after cell climbing.
(A, B and C correspond to Anti-SLC17A2 scFvs No.1, No.2 and No.3 delivered by Xi'an Haina Biotechnology Co., Ltd., all conjugated with Cy-3.)
(A, B and C correspond to Anti-SLC17A2 scFvs No.1, No.2 and No.3 delivered by Xi'an Haina Biotechnology Co., Ltd., all conjugated with Cy-3.)
(III) Optimization and functional validation of the Syn-M based on Anti-SLC17A2 scFv
| Design | The SynNotch system consists of a ligand-binding domain (LBD), extracellular domain (ECD), transmembrane domain (TMD), juxtamembrane domain (JMD), and transcription factor (TF). Utilizing the acquired Anti-SLC17A2 scFv, we aimed to modify the corresponding domains of the SynNotch system. |
| Build | We designed the Syn-M-INPUT Test 1 system by employing the Anti-SLC17A2 scFv as the Ligand-Binding Domain (LBD), GV as the intracellular Transcription Factor (TF), and retaining the Notch 1 structure for the remaining domains (Figure 10). |
| Test | When designing the Syn-M-INPUT Test 1 system based on the mouse Notch1 human equivalent, we observed high ligand-independent signaling (i.e., basal activation) in the original design, meaning partial self-activation occurred even in the absence of the SLC17A2 signal on hepatocytes (Figure 11). |
| Learn | Encouraged by these results, we considered replacing the retained Notch1 structural components to improve signal activation efficiency and receptor sensitivity. |
Figure 10. Design diagram of the Syn-M-INPUT Test 1 System.
(A: Design diagram of the Syn-M-INPUT Test 1 System; B: Structural design diagram of the plasmid used in the Syn-M-INPUT Test 1 system; C: Lentivirus packaging and cell infection schematic diagram)
(A: Design diagram of the Syn-M-INPUT Test 1 System; B: Structural design diagram of the plasmid used in the Syn-M-INPUT Test 1 system; C: Lentivirus packaging and cell infection schematic diagram)
Figure 11. Basal activation of the Syn-M-INPUT Test 1 System in the absence of hepatocyte SLC17A2 signal.
| Design | Through a literature review, we decided to adopt a systematic modular engineering approach to modify the extracellular domain (ECD) of the existing Syn-M-INPUT Test 1 system, aiming to reduce its basal self-activation level. |
| Build | We replaced the Notch1 extracellular region sequence with the CD8α hinge extracellular sequence, while retaining the mouse Notch1 transmembrane domain and cleavage site (Figure 12). Concurrently, to validate the specificity of the optimized system, we performed immunofluorescence and other experiments on the Syn-M-INPUT Test 2 system. |
| Test | The engineered SynNotch receptor (Syn-M-INPUT Test 2 system) was 810 bp shorter than the original full-length design (Syn-M-INPUT Test 1 system). Experimental results showed that the system did not self-activate in the absence of the hepatocyte SLC17A2 signal. Upon receiving the protein signal, the system displayed strong fluorescence, indicating its specific activation capability (Figure 13). |
| Learn | We replaced the ECD module component with one demonstrating superior performance (the optimized CD8α hinge). This optimized CD8α hinge SynNotch receptor was selected for subsequent development and chassis cell engineering due to its efficient, high-fidelity activation characteristics and compact sequence length. |
Figure 12. Design diagram of the Syn-M-INPUT Test 2 System.
Figure 13. Fluorescence Microscopy Detection after Co-culture of the Anti-SLC17A2 scFv-based SynNotch-test-Version2 System with mCherry-AML12 Cells
(Macrophage: AML12 cell ratio = 1:2, co-culture time = 24h).
(Macrophage: AML12 cell ratio = 1:2, co-culture time = 24h).
Cycle-3
OUTPUT Module-Constructing a P65-SIRPα shRNA Bicistronic System
| Design | To address the other major challenge-the immunosuppressive tumor microenvironment-we needed to regulate intracellular gene expression in macrophages to promote the expression of pro-inflammatory and anti-cancer related genes. |
| Build | Literature review indicated that P65 (RelA) is a key anti-cancer pro-inflammatory target (Figure 14). Therefore, we selected it as the regulatory target for macrophage gene expression. Upon activation in the liver environment, the GV domain from the SynNotch intracellular segment is cleaved and enters the nucleus, enhancing P65 expression. This action, via the NF-κB pathway, induces the secretion of inflammatory factors like TNF-α and IL-1β, potentially reversing immunosuppressive M2 macrophages to an anti-tumor M1 phenotype, recruiting CD8+ T cells, enhancing anti-tumor immunity, and improving the immune microenvironment. |
| Test | Through communication with our PI [HP], we learned that the SIRPα-CD47 pathway is a crucial axis for tumor cell immune evasion (Figure 15). Tumor cells express CD47, which binds to SIRPα on macrophages, transmitting a "don't eat me" signal, contributing to the formation of an immunosuppressive TME. |
| Learn | Therefore, we considered dual regulation of both SIRPα and P65 in macrophages, aiming to upregulate anti-tumor gene expression and downregulate pro-tumor gene expression, achieving a synergistic anti-tumor effect. |
Figure 14. P65/NF-κB Pathway((Image source: immunopharmacology.2021;101,Pt B:108255.))
Figure 15. SIRPα-CD47 Pathway((Image source: J Hepatol. 2022 Dec;77(6):1598-1606.))
| Design | To achieve simultaneous upregulation of anti-tumor genes and downregulation of pro-tumor genes, we identified that the silencing and degradation effect of shRNA could indirectly inhibit SIRPα expression. Accordingly, after GV enters the nucleus and synchronously activates the expression of both P65 and SIRPα shRNA, upregulation of P65 and downregulation of SIRPα can be achieved. |
| Build | Since mRNA and shRNA are transcribed by RNA Polymerase II (Pol II) and Pol III respectively, we designed a bicistronic system. The SIRPα shRNA was flanked by 5' and 3' miRNA flanking sequences (Figure 16), enabling it to utilize Pol II transcription linkage sites, thus allowing synchronous transcription of P65 and SIRPα shRNA. |
| Test | To verify the regulation of P65 and SIRPα gene expression, we performed immunofluorescence detection on Syn-M cells. Results showed that compared to the control group, the Syn-M group exhibited upregulated P65 expression and downregulated SIRPα expression (Figures 17, 18). |
| Learn | Through theoretical design and a series of validation experiments, we preliminarily achieved the goal of synergistic anti-tumor action via upregulation of anti-tumor and downregulation of pro-tumor signals. Our final design schematic is as follows: the INPUT module achieves liver-specific activation based on Anti-SLC17A2 scFv, and the OUTPUT module simultaneously expresses P65 and SIRPα shRNA to function synergistically against the immunosuppressive microenvironment. We named this system "Synergy" (also be referred to as Syn-M when describing experimental results). Admittedly, subsequent demonstration of the in vivo anti-tumor efficacy of Synergy is also crucial. |
Figure 16. Design diagram of Synergy.
(A: Design diagram of Synergy; B: Structural design diagram of the plasmid used in Synergy)
(A: Design diagram of Synergy; B: Structural design diagram of the plasmid used in Synergy)
Figure 17. Immunofluorescence staining for F4/80 and P65 after co-culture of Syn-M with AML12 cells on coverslips
(Macrophage : AML12 cell ratio = 1:2, co-culture time 24h).
(Macrophage : AML12 cell ratio = 1:2, co-culture time 24h).
Figure 18. Immunofluorescence staining for F480 and SIRPα after co-culture of Syn-M with AML12 cells on coverslips
(Macrophage : AML12 cell ratio = 1:2, co-culture time 24h).
(Macrophage : AML12 cell ratio = 1:2, co-culture time 24h).
Cycle-4
Delivery Method Optimization
| Design | We recognized that autologous macrophage-based therapies, involving ex vivo modification and reinfusion, have several limitations, including high production costs, stringent cell storage and transportation requirements, and limited sources of recipient-autologous macrophages. We needed to consider how to optimize the delivery method to better address these limitations and lower the treatment threshold. |
| Build | Through literature research and expert consultation [HP], we found that Lipid Nanoparticle (LNP) delivery systems offer advantages such as excellent biocompatibility, high modifiability, and simplified storage and transportation conditions, which could compensate for several shortcomings of the autologous macrophage reinfusion strategy. Based on this, we planned to utilize LNP as a delivery vehicle to encapsulate the Syn-M related plasmids, leveraging LNP for efficient and stable in vivo delivery and gene editing. |
| Test | We utilized the ionizable property of DLin-MC3-DMA and the hepatocellular carcinoma-associated macrophage targeting capability of DSPE-PEG-M2pep (Figure 19) to enhance the editing efficacy of the LNP. The LNP contains components for the in situ editing of tumor-associated macrophages (TAMs) into Syn-M, fulfilling our design objective. Furthermore, using techniques like transmission electron microscopy (TEM), we verified that the liposomes were spherical, of appropriate size (~85 nm), with uniform particle size distribution, and essentially electrically neutral (Figure 20), meeting the preliminary design requirements. |
| Learn | Our current design for Synergy@LNP is at the initial stage, and we plan to conduct subsequent experimental validation after the competition. |
Figure 19. Design diagram of LNP for in situ editing of TAMs into Syn-M at the liver cancer Site.
(Image adapted from: https://www.malvernpanalytical.com.cn/industries/biologics/lipid-nanoparticles.)
Figure 20. A: Size, PDI, Zeta Potential (A) and representative transmission electron microscopy image (B) of Syn-M@LNP.
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