Engineering
Introduction
Our project is dedicated to developing an mRNA-based therapeutic strategy for liver cirrhosis that utilizes negative feedback regulation to modulate S-adenosylmethionine levels. Throughout the development process, we have closely adhered to the engineering cycle recommended by iGEM. Each cycle consists of four key phases: Design, Build, Test, and Learn. Thanks to well-defined roles and efficient collaboration, the project progressed through rapid iterations. Most challenges encountered along the way were resolved in a timely and effective manner, enabling us to ultimately achieve the outcomes envisioned at the initial design stage.
Engineering 1: Construction of SAM I/VI Aptazyme
Cycle 1: Selection of Aptazymes
Design:
Aptazymes serve as our core effector system. The HDV ribozyme1 and hammerhead ribozyme2 are currently the most widely used and versatile platforms for engineering aptazymes. To date, various HDV and hammerhead aptazymes capable of sensing small molecules such as guanine have been developed. Based on our literature review, replacing the aptamer domains of HDV and hammerhead ribozymes enables the engineering of aptazymes that respond to other small molecules. Therefore, we decided to select HDV and hammerhead ribozymes with high cleavage efficiency for further modification.
We validated the responsiveness of the guanine-specific hammerhead and HDV aptazymes by employing them as sensors, where guanine binding serves as the signal input and EGFP expression as the output. Specifically, we inserted these aptazymes into the 3'UTR of mRNA. If the aptazyme undergoes self-cleavage, the mRNA loses its structural integrity and is degraded by nucleases, resulting in no EGFP expression. If no self-cleavage occurs, the mRNA remains stable and EGFP is expressed normally. The responsiveness of the system was evaluated under low guanine (normal culture conditions) and high guanine (500 μM guanine treatment) concentrations, and the dynamic range of EGFP expression—represented by the ON/OFF ratio—was used to quantify the system's performance.
Build:
We designed the following plasmids: plasmid-EGFP-aptazyme-Gua-HDV and plasmid-EGFP-aptazyme-Gua-Hammer, which incorporate guanine-specific hammerhead and HDV ribozymes respectively, coupled with EGFP; along with a control plasmid (plasmid-EGFP-BLANK) lacking any aptazyme element, as illustrated in the figure below. These plasmids were transfected into HEK293T cells to validate their responsiveness and cleavage efficiency.
Test:
The plasmids plasmid-EGFP-aptazyme-Gua-HDV, plasmid-EGFP-aptazyme-Gua-Hammer, and plasmid-EGFP-BLANK were complexed with PEI and transfected into HEK293T cells. For each plasmid, two conditions were established: one group received DMEM supplemented with 500 μM guanine at 8 hours post-transfection, while the other was maintained in DMEM alone as a control. EGFP expression was observed under a fluorescence microscope at 24 hours post-transfection.
Conclusion: The aptazyme-Gua-HDV demonstrated superior gene expression control in response to guanine, with an ON/OFF ratio of 11.7-fold, whereas the aptazyme-Gua-Hammer showed only a 1.2-fold ON/OFF ratio.
Learn:
We evaluated the ON/OFF efficiency of the HDV and hammerhead aptazymes in response to guanine, and found that the aptazyme-Gua-HDV exhibited a significantly higher ON/OFF ratio compared to the aptazyme-Gua-Hammer. Consequently, we selected the HDV aptazyme platform for subsequent engineering.
Cycle 2: Selection of SAM Riboswitch
Design:
To enable our aptazyme to undergo allosteric self-cleavage in response to SAM, we attempted to engineer a functional aptazyme by fusing a SAM riboswitch with the HDV ribozyme. We conducted a thorough review of SAM riboswitch fundamentals and consulted with Dr. Wenwen Xiao from Donghua University. Based on her recommendation, we selected the structurally simpler SAM-I3 and SAM-VI4 riboswitches for this purpose. Subsequently, we designed fusion constructs by integrating the SAM-binding aptamer domains of the SAM-I and SAM-VI riboswitches with the HDV ribozyme.
Build:
We constructed plasmids encoding SAM-responsive aptazymes fused to EGFP, plasmid-EGFP-Aptazyme SAM I and plasmid-EGFP-Aptazyme SAM VI-8, as shown in the figure. These plasmids were transfected into HEK293T cells to evaluate their responsiveness and cleavage efficiency. For each plasmid, two experimental conditions were established: one group received DMEM supplemented with 500 μM SAM at 8 hours post-transfection, while the other group was maintained in standard DMEM as a control. EGFP expression levels were observed under a fluorescence microscope at 24 hours post-transfection.
Test:
Based on the EGFP expression observed via fluorescence microscopy in HEK293T cells, we found that Aptazyme SAM-I exhibited no self-cleavage activity under either physiological intracellular SAM concentrations or high SAM conditions with extracellular SAM supplementation. In contrast, Aptazyme SAM-VI demonstrated self-cleavage under both conditions, though it lacked concentration-dependent responsiveness.
Learn:
Compared to the guanine aptamer, the aptamer domain of the SAM-I riboswitch, with its considerably longer sequence (94 nt vs. 57 nt for guanine aptamer), may prevent the formation of a functional HDV ribozyme structure when inserted into the aptazyme scaffold, thereby impairing self-cleavage activity. We therefore conclude that the SAM-I riboswitch is unsuitable for integration into the HDV ribozyme.
The SAM-sensing threshold of Aptazyme SAM-VI is likely below the physiological SAM concentration in HEK293T cells. As a result, the aptazyme undergoes self-cleavage even under normal SAM conditions, leading to minimal eGFP expression in both normal and high SAM conditions, as observed in the fluorescence images.
These findings prompted us to optimize intracellular SAM concentrations for further testing. Meanwhile, literature research revealed structural similarities between the SAM-III and SAM-VI riboswitches.5 We therefore plan to test the SAM-III riboswitch and evaluate whether Aptazyme SAM-III exhibits superior concentration-dependent responsiveness to SAM compared to Aptazyme SAM-VI.
Cycle 3: Establishment of a HEK293T Cell Model with Varying SAM Concentrations
Design:
Based on our findings from the previous cycle, we learned that the response threshold of Aptazyme SAM-VI is likely lower than the normal SAM concentration in HEK293T cells. To develop a SAM-responsive aptazyme, it is essential to establish a cell model with a gradient of SAM concentrations.
Build:
SAM synthesis in normal mammalian cells is primarily catalyzed by the enzyme MAT2A. Inhibiting MAT2A activity can effectively reduce intracellular SAM concentration.
Cycloleucine is a known inhibitor of MAT2A. Therefore, we decided to use cycloleucine6 and SAM supplementation to establish HEK293T cell models with low, normal, and high intracellular SAM concentrations.
Test:
We established the following culture conditions: DMEM supplemented with 30 mM cycloleucine (cLeu) to downregulate intracellular SAM levels; standard DMEM for HEK293T cells with physiological SAM concentration; and DMEM with 500 μM SAM to create a high-SAM model. Mass spectrometry was employed to determine the relative SAM concentrations.
Compared to the standard DMEM group, HEK293T cells treated with 30 mM cLeu showed a reduction in SAM concentration to 0.71-fold, while those treated with 500 μM SAM exhibited an increase to 1.44-fold.
Learn:
In this cycle, we successfully established cell models with both sub-physiological and supra-physiological SAM concentrations, laying the foundation for the subsequent evolution of SAM-responsive aptazymes. The responsiveness of these aptazymes to SAM was evaluated by monitoring their ON/OFF ratios across cell models with varying SAM concentrations.
Cycle 4: Construction of Aptazyme-SAM VI/III
Design:
The SAM VI-HDV aptazyme obtained in Cycle 2 did not demonstrate satisfactory concentration-dependent responsiveness in cell models with SAM levels at or above the physiological concentration. Therefore, we plan to evaluate the SAM-responsive capability of the SAM VI-HDV aptazyme using the SAM concentration gradient cell models established in Cycle 3.
Concurrently, we fused the aptamer domain of the SAM-III riboswitch with the HDV ribozyme to generate a novel SAM-III-HDV aptazyme, which will be tested to determine whether it exhibits superior sensitivity and concentration-dependent response compared to the SAM VI-HDV aptazyme. Both the SAM-III-HDV and SAM VI-HDV aptazyme constructs were inserted into the 3'UTR region and validated across the three SAM concentration gradient cell models developed in Cycle 3.
Build:
Test:
We observed the expression of EGFP in HEK293T cells with different SAM concentrations using a fluorescence microscope. For Aptazyme SAM-VI, EGFP expression was significantly higher at low SAM concentrations compared to both normal and high SAM levels, yielding an ON/OFF ratio of 10.7-fold. However, no significant difference in EGFP expression was observed between normal and high SAM conditions. In contrast, Aptazyme SAM-III showed no notable differences in EGFP expression across low, normal, and high SAM concentrations.
Learn:
The aptazyme component Aptazyme-SAM VI demonstrates measurable responsiveness to SAM concentration, indicating potential for further optimization. In contrast, Aptazyme-SAM III shows negligible response to SAM variations. We therefore aim to rationally redesign these components to enhance their ON/OFF ratios. Furthermore, we compared the secondary structures of Aptazyme-SAM III, Aptazyme-SAM VI, and the gua-HDV aptazyme from Cycle 1. Notable differences were observed in the number of linker nucleotides between the aptamer and ribozyme domains: the gua-HDV aptazyme contains 4 linker nucleotides, while SAM-III-HDV and SAM-VI-HDV aptazymes possess 2 and 8, respectively. We hypothesize that the number of these linker nucleotides influences the ON/OFF ratio of the components.
Engineering 2: Evolution of SAM Aptazyme with SAM Concentration Response
Cycle 1: Optimization of the Number of Base Pairs Linked by Aptamers and Ribozymes
Design:
To optimize the aptazymes, we discussed the findings from Engineer 1 with the dry lab team: the linker base pairs between the aptamer and ribozyme in Aptazyme-SAM VI and Aptazyme-SAM III may be too long. We aim to investigate the relationship between the number of linker base pairs and aptazyme functionality, and have requested their assistance in predicting the secondary structures of the aptazymes.
Build:
We constructed EGFP plasmids containing Aptazyme SAM-VI with six different linker lengths (0, 2, 4, 6, 8, and 16 base pairs) and Aptazyme SAM-III with four linker lengths (1, 2, 3, and 4 base pairs). These plasmid constructs were subsequently transfected into HEK293T cells to evaluate their responsiveness and cleavage efficiency.
Test:
Aptazyme SAM-VI-0, -2, and -4 showed no self-cleavage activity across all SAM concentration cell models. In contrast, Aptazyme SAM-VI-6 and -16 underwent self-cleavage under all SAM conditions. None of the Aptazyme SAM-VI variants with 2, 4, 6, or 16 linker base pairs exhibited concentration-dependent responsiveness to SAM. However, Aptazyme SAM-VI-8 demonstrated a clear concentration-dependent response, with EGFP fluorescence intensity gradually decreasing from low to high SAM concentrations, yielding an ON/OFF ratio of 10.7.
For the SAM-III series, Aptazyme SAM-III-3 showed only minimal concentration responsiveness between normal and high SAM conditions, with an ON/OFF ratio of 1.2-fold, which is substantially lower than that of Aptazyme SAM-VI-8.
Learn:
Aptazyme SAM-VI-8 demonstrated concentration-dependent responsiveness to SAM, indicating it possesses the optimal number of linker base pairs. While Aptazyme SAM-III-3 also exhibited some degree of SAM responsiveness, the ON/OFF ratios of both constructs were not ideal. We therefore selected Aptazyme SAM-VI-8 for further optimization. We hypothesize that modifying the composition of the linker base pairs may lead to improved performance.
Cycle 2: Optimization of Base Pair Combinations in Aptazyme SAM VI-8
Iteration 1: Integrate P0 into Aptazyme SAM VI-8
Design:
We held another discussion with Dr. Wenwen Xiao from Donghua University, during which she mentioned that the P0 stem of the SAM-VI riboswitch contributes to SAM binding and allosteric regulation. She suggested that integrating the P0 stem into Aptazyme SAM-VI-8 might enhance the component's ON/OFF ratio.7
Build:
We constructed the P0-integrated aptazyme, designated Aptazyme SAM-VI-P0, and transfected it into HEK293T cells with varying SAM concentrations for functional validation.
Test:
Aptazyme SAM-VI-P0 exhibited an ON/OFF ratio of 2.5-fold, whereas Aptazyme SAM-VI-8 demonstrated a significantly higher ON/OFF ratio of 9.4-fold.
Learn:
The incorporation of the P0 stem into Aptazyme SAM-VI-8 did not improve its ON/OFF ratio; instead, it resulted in a reduction.
Iteration 2: Base Combination Screening
Design:
We consulted with Professor Yanqiu Shao from Xunjing Biotechnology, who explained that their methodology for predicting small molecule-aptamer binding involves first generating extensive wet-lab data on molecular interactions, then using this data to train and iteratively refine a model, ultimately developing a large-scale predictive model. Inspired by this approach, we decided to collaborate with computational teams to further optimize our components.
We reviewed literature on the gua-HDV aptazyme and selected the top ten base-pair combinations previously validated in published studies, applying these to our Aptazyme SAM-VI framework. Additionally, we engaged with dry-lab colleagues with the aim of utilizing their computational predictions for SAM and Aptazyme SAM-VI binding to guide our subsequent experimental optimization. Ultimately, we constructed and tested 20 distinct linker base-pair variants of Aptazyme SAM-VI.
Build:
We constructed 20 plasmid variants of Aptazyme SAM-VI, each featuring a distinct linker base-pair combination. The responsiveness of these aptazymes to SAM was subsequently validated by transfecting the plasmids into HEK293T cells.
Test:
We identified four components that demonstrated significant efficacy: Aptazyme SAM-VI-8 (ON/OFF ratio: 9.4-fold), Aptazyme SAM-VI-8-15 (7.5-fold), Aptazyme SAM-VI-8-16 (8.5-fold), and Aptazyme SAM-VI-8-20 (5.3-fold). All exhibited substantial responsiveness to SAM concentration variations.
Learn:
The obtained aptazymes – SAM-VI-8, SAM-VI-8-15, SAM-VI-8-16, and SAM-VI-8-20 – demonstrated significant SAM concentration-dependent responsiveness in HEK293T cells. To evaluate their therapeutic potential for cirrhosis, we proceeded to validate their performance in a cirrhotic cell model.
Engineering 3: Validation in a Cirrhotic Cell Model
Cycle 1: Establishment of a Cirrhotic Cell Model
Iteration 1: The Relative Expression Levels of MAT1A and MAT2A
Design:
To establish a hepatocyte model of liver fibrosis, we consulted with Professor Lu Fengmin, an expert in the field, and reviewed relevant literature. It was understood that the synthesis of S-adenosylmethionine (SAM) in hepatocytes is co-regulated by the MAT1A and MAT2A genes. Based on these findings, we plan to validate the relative expression levels of MAT1A and MAT2A in subsequent studies.
Build:
We selected qPCR primers for MAT1A, MAT2A, and GAPDH from PrimerBank, all with comparable amplicon lengths. We used them for qPCR verification
Test:
Based on the comparison of MAT1A and MAT2A expression levels in HepG2 cells, we observed that MAT2A expression was significantly higher than that of MAT1A, which is consistent with our expected results.
Learn:
Based on the expression profile observed in HepG2 cells, we propose that inhibiting MAT2A could serve as a viable strategy for constructing a cirrhotic cell model with low SAM concentration.
Iteration 2: Preliminary Validation of Liver Cirrhosis Cell Models
Design:
In HepG2 cells, the expression level of MAT2A is relatively high, while that of MAT1A is relatively low. We still used the method of adding a small molecule inhibitor of MAT2A to inhibit the enzymatic activity of MAT2A, thereby constructing a liver cirrhosis cell model.
Build:
Consistent with the approach in Experiment 2, we supplemented the cultures with cycloleucine and SAM, respectively, to model different pathological and normal physiological conditions. We quantified the intracellular SAM concentration using mass spectrometry.
Test:
We established cirrhotic model groups by treating cells with DMEM supplemented with either 60 mM or 30 mM cycloleucine (cLeu) to inhibit MAT2A enzyme activity. A control group was cultured in untreated DMEM. Additional groups were treated with DMEM containing 0.5 mM SAM or 1 mM SAM. All HepG2 cells were harvested after 24 hours for mass spectrometry analysis.
The mass spectrometry results showed that both 60 mM and 30 mM cycloleucine treatments increased intracellular SAM concentration compared to the DMEM control group. Furthermore, treatment with 0.5 mM and 1 mM SAM elevated intracellular SAM levels by 3.8-fold and 11.5-fold, respectively, relative to the DMEM control.
Learn:
Cells treated with DMEM supplemented with either 60 mM or 30 mM cycloleucine (cLeu) showed intracellular SAM concentrations higher than those in the DMEM-only control group. Initially, we suspected potential operational errors in our experiment. However, the results from the DMEM + 0.5 mM SAM and DMEM + 1 mM SAM treated HepG2 cells aligned with our expectations, as both groups exhibited increased intracellular SAM concentrations.
We therefore considered that cycloleucine, being prone to degradation, may have been largely depleted by the 24-hour sampling time point. While a decrease in SAM concentration may trigger a feedback upregulation of MAT2A expression, which in turn catalyzes SAM synthesis, leading to an elevated SAM level at the 24-hour time point. Optimizing the duration of cycloleucine treatment may potentially enable the establishment of a low-SAM cirrhotic cell model.
Iteration 3: Optimization of the Liver Cirrhosis Cell Model Conditions
Design:
Based on the consideration that MAT2A may undergo feedback upregulation, we aim to optimize the cycloleucine treatment duration to measure intracellular SAM concentration before the inhibitor loses efficacy and before MAT2A becomes significantly overexpressed.
Build:
We established five distinct treatment durations (2, 6, 10, 16, and 32 hours) with DMEM supplemented with 30 mM cycloleucine (cLeu). The abundance of MAT2A mRNA was assessed by qPCR to identify the optimal treatment time point. Subsequently, LC-MS was performed to determine whether intracellular SAM concentration decreased at this selected time point.
Test:
- 1. qPCR
Following treatment with DMEM supplemented with 30 mM cycloleucine (cLeu), the expression of MAT2A in HepG2 cells initially increased and then decreased with prolonged exposure time. Therefore, we selected the 2-hour time point for subsequent experiments. According to LC-MS results, the intracellular SAM concentration in the 2-hour DMEM + 30 mM cLeu treatment group was reduced to 26% of that in the DMEM-only control group.
Figure 24. Relative MAT2A mRNA Abundance in HepG2 Cells - 2. LC-MS
The intracellular SAM concentration in the 2-hour 30 mM cLeu treatment group was reduced to 26% of that in the DMEM control group.
Figure 25. Quantification of Intracellular SAM Concentration in HepG2 Cells by LC-MS
Learn:
We have established optimized conditions for constructing a cirrhotic cell model, which can be utilized for subsequent validation of therapeutic interventions for cirrhosis.
Cycle 2: Plasmid Verification
Iteration 1: EGFP-ReguSAMe Plasmid Reporting Experiment
Design:
We validated the SAM-responsive capability of four aptazymes – Aptazyme SAM-VI-8, SAM-VI-8-15, SAM-VI-8-16, and SAM-VI-8-20 – in a cirrhotic cell model.
Build:
We transfected the following plasmids into hepatic cells: plasmid-EGFP-Aptazyme SAM-VI-8, plasmid-EGFP-Aptazyme SAM-VI-8-15, plasmid-EGFP-Aptazyme SAM-VI-8-16, and plasmid-EGFP-Aptazyme SAM-VI-8-20.
Test:
- 1. Fluorescence microscope
The plasmids encoding Aptazyme SAM-VI-8, SAM-VI-8-15, SAM-VI-8-16, and SAM-VI-8-20 were transfected into both cirrhotic cell models and normal HepG2 cells. Fluorescence microscopy revealed the following ON/OFF ratios in hepatic cells: Aptazyme SAM-VI-8 (3.1-fold), SAM-VI-8-15 (3.1-fold), SAM-VI-8-16 (2.6-fold), and SAM-VI-8-20 (1.9-fold).
Figure 27. Fluorescence Microscopy Image 
Figure 28. Assessment of SAM Responsiveness of ReguSAMe in HepG2(plasimd).Under low SAM conditions (30 mM cLeu treatment) versus high SAM conditions (normal culture), the responsiveness of the device was quantified as the dynamic range of EGFP expression, represented by the ON/OFF ratio. - 2. Flow cytometer
To account for potential fluorescence intensity variations caused by differences in cell numbers across experimental groups, we performed flow cytometry to further validate the reliability of our components. The results were consistent with the fluorescence microscopy observations, confirming that all three ReguSAMe constructs exhibited SAM responsiveness.
Figure 29. Flow Cytometric Analysis of EGFP Expression in HepG2 Cells Transfected by Plasmid 
Figure 30. Assessment of SAM Responsiveness of ReguSAMe in HepG2(plasmid).The response of ReguSAMe to SAM in cells was validated by quantifying EGFP expression as the percentage of positive cells within the parent population using flow cytometry.
Learn:
Based on fluorescence imaging and flow cytometry data, the constructs Aptazyme-SAM VI-8, VI-8-15, and VI-8-16 demonstrated a robust response to SAM in hepatocytes. We will therefore attempt to replace the EGFP gene with MAT1A, the enzyme that catalyzes SAM synthesis, thereby constructing a negative feedback loop. This system is designed to restore SAM concentrations to the normal physiological range, and its therapeutic feasibility will be validated.
Iteration 2: MAT1A-ReguSAMe Plasmid Verification
Design:
The EGFP reporter gene is substituted with MAT1A to construct a negative feedback circuit sensing SAM. The responsiveness of the Aptazyme-SAM constructs (VI-8, VI-8-15, and VI-8-16) to SAM will subsequently be assessed based on the expression level of MAT1A mRNA.
Build:
We constructed the following plasmids: plasmid-MAT1A-Aptazyme SAM-VI-8, plasmid-MAT1A-Aptazyme SAM-VI-8-15, plasmid-MAT1A-Aptazyme SAM-VI-8-16, and plasmid-MAT1A-blank. These constructs were transfected into hepatic cells using Lipofectamine 3000, and MAT1A mRNA abundance under different conditions was quantified by qPCR.
Test:
Over the period of 16 to 48 hours, ReguSAMe successfully maintained intracellular MAT1A at a stable, elevated level without causing the overexpression observed in the BLANK group.
Learn:
ReguSAMe successfully maintained intracellular MAT1A at a stable, elevated level without causing the overexpression observed in the BLANK group. With the negative feedback circuit successfully constructed, we next aim to pave the way for developing drug-developable mRNA therapeutics.
However, due to the inherent limitation of slow expression kinetics in the plasmid system, we were unable to obtain reliable MAT1A mRNA levels at earlier time points. Consequently, we switched to an mRNA-based delivery approach to investigate the therapeutic potential of ReguSAMe for cirrhosis.
Cycle 3: mRNA Verification
Iteration 1: Lipid Nanoparticle (LNP) Delivery System
Design:
In order to find delivery carriers that can carry our mRNA, we communicated with Professor Lei Miao, an expert in drug delivery. We learned that Lipid nanoparticles (LNP) are one of the most widely used delivery systems in today's gene drugs, mainly for liver delivery and vaccine applications. To build a complete therapeutic system, we need to prepare LNP delivery carriers to encapsulate our mRNA.
Build:
DLin-MC3-DMA is a commonly used ionizable cationic lipid that becomes protonated under acidic conditions to form a positive charge, enabling binding with negatively charged mRNA and facilitating particle formation and cell membrane fusion. We employed distearoylphosphatidylcholine (DSPC) to modulate the fluidity of the LNP, thereby enhancing fusion with cell membranes. Cholesterol was incorporated to fill the gaps between lipids and improve the stability of the LNPs. The encapsulation of mRNA into LNPs was achieved via a manual mixing method.
Test:
We used dynamic light scattering (DLS) to measure the particle size and uniformity of the LNP, achieving a controlled particle size distribution of 80–120 nm.
Learn:
We have successfully prepared LNP, which provides an efficient platform for our subsequent mRNA delivery.
Iteration 2: EGFP mRNA Reporting Experiment
Design:
To observe the cutting efficiency of our aptamer enzyme, we verified it by combining it with EGFP and transcribing it into mRNA in vitro.
Build:
We performed in vitro transcription using a double-stranded DNA template containing a T7 promoter sequence. The transcription was initiated from the T7 promoter, and the cap analog Cap1AG was co-transcriptionally incorporated into the 5' end of the mRNA, resulting in single-stranded RNA with a 5'-m7G Cap1 structure. The Cap1 cap structure enhances RNA stability, thereby protecting the RNA from degradation within cells.
Test:
- 1. Fluorescence microscopy characterization
We captured the fluorescence signals of each group of HepG2 cells under a fluorescence microscope and conducted quantitative analysis of the relative fluorescence intensities. We found that the fluorescence intensities of Aptazyme SAM VI-8-16 and Aptazyme SAM VI-8-15 were significantly higher at low SAM concentrations than at the normal physiological SAM concentration. The experiment preliminarily proved that our aptamer enzyme elements can respond to high SAM concentrations by undergoing cleavage.
Figure 33. Fluorescence Microscopy Image - 2.Flow cytometer
To preclude fluorescence intensity variations caused by differing cell counts across groups, we performed flow cytometry to further validate the reliability of our components. The results were consistent with the fluorescence microscopy observations, with Aptazyme SAM VI-8-16 demonstrating the optimal concentration-dependent response.
Figure 34. Assessment of SAM Responsiveness of ReguSAMe in HepG2(mRNA).Under low SAM conditions (30 mM cLeu treatment) versus high SAM conditions (normal culture), the responsiveness of the device was quantified as the dynamic range of EGFP expression, represented by the ON/OFF ratio. 
Figure 35. Flow Cytometric Analysis of EGFP Expression in HepG2 Cells Transfected by mRNA 
Figure 36. Assessment of SAM Responsiveness of ReguSAMe in HepG2(mRNA).The response of ReguSAMe to SAM in cells was validated by quantifying EGFP expression as the percentage of positive cells within the parent population using flow cytometry.
Learn:
The EGFP reporter assay validated the functionality of our engineered component within the mRNA. We could replace the EGFP gene with the MAT1A enzyme, which catalyzes SAM synthesis, to attempt to restore SAM concentration to the normal physiological level and test the therapeutic feasibility of this approach.
Iteration 3: mRNA-MAT1A Verification
Design:
We designed an mRNA construct encoding MAT1A that incorporated our aptazyme element, to test its ability to restore SAM to physiological concentrations.
Build:
We modified only the coding sequence (CDS) of the mRNA by substituting the EGFP gene with MAT1A. The mRNA was then produced by in vitro co-transcription, as described in interation 2 previously.
Test:
When the SAM concentration reaches the normal physiological level, our aptazyme component undergoes self-cleavage. To determine the time point at which the SAM concentration is restored to the physiological range, we quantified the remaining levels of delivered MAT1A mRNA in cells at different time points using qPCR.
- 1. qPCR
We delivered MAT1A mRNA with Aptazyme SAM VI, Aptazyme SAM VI-8-15, and Aptazyme SAM VI-8-16 elements to liver cirrhosis cell models and normal SAM physiological concentration cell models. At 3 hours, in the liver cirrhosis cell model, the aptamase sensed a low SAM concentration and did not perform cleavage, while in the normal SAM concentration cell model, the aptamase performed cleavage. With the expression of MAT1A and its catalytic synthesis of SAM, the SAM concentration in the liver cirrhosis cell model increased, and the remaining amounts of MAT1A mRNA in both groups tended to be the same, indicating that the SAM concentration had returned to the normal physiological concentration at this time. Thus, it was determined that the subsequent mass spectrometry sample collection time would be 6 hours.
Figure 37. Relative MAT1A mRNA Abundance in HepG2 Cells Transfected with mRNA (A) mRNA inserted Aptazyme SAM VI-8. (B) mRNA inserted Aptazyme SAM VI-8-15. (C) mRNA inserted Aptazyme SAM VI-8-16. - 2. LC-MS
Based on the qPCR results, we confirmed that Aptazyme SAM VI-8-15 exhibited the most rapid response and maintained a cleavage rate closest to that of the DMEM group after SAM concentration was restored. This aptazyme was therefore selected for further validation by mass spectrometry.
To control for potential effects of the mRNA delivery process itself on intracellular SAM levels, we delivered EGFP mRNA as a control. We then delivered MAT1A mRNA either equipped with or without the Aptazyme SAM VI-8-15 element and monitored the subsequent changes in intracellular SAM concentration, with samples collected at 6 hours.
The results demonstrated that the incorporation of our aptazyme element led to a significant increase in intracellular SAM concentration, which remained within the normal physiological range. In contrast, in the group receiving MAT1A mRNA without the Aptazyme SAM VI-8-15 element, the intracellular SAM concentration substantially exceeded the normal physiological level.
Figure 38. Quantification of Intracellular SAM Concentration in HepG2 Cells Transfected with Different mRNA by LC-MS
Learn:
Although the experimental results aligned with our expectations, the SAM concentration did not reach the normal physiological level. This could be attributed to the short sampling time, which may not have allowed sufficient duration for MAT1A to elevate SAM to its target concentration. Due to time constraints imposed by the iGEM competition schedule, we were unable to investigate the relationship between SAM concentration and time.