Modular Transcriptional Regulation Using Switchable Transcription Terminators and Aptamers: Design and Optimization in Synthetic Biology

3.1. Characterization of SWT Performance SWT (switchable transcription terminator) is a type of transcriptional regulatory element based on the dynamic structural changes of RNA. Its core principle relies on the variability of RNA secondary structures, enabling controlled transcription termination or resumption in response to specific input signals (e.g., trigger RNA). Previous studies have optimized the structure and algorithm of SWT. To further investigate the transcriptional regulation efficiency of SWT and achieve precise control over the transcription process, we need to validate the SWT sequences generated by the algorithm. An ideal SWT should exhibit low leakage, high ON/OFF ratio, and good orthogonality. In this study, to screen for high-performance SWTs, we linked a series of algorithm-generated SWT sequences to the reporter gene 3WJdB and analyzed their performance using an in vitro transcription system (Figure 2A). In the absence of trigger RNA (OFF state), a small amount of the reporter gene (3WJdB) may still be transcribed. The fluorescent signal generated by the transcribed 3WJdB binding to DFHBI-1T is referred to as the leakage value. A low leakage value indicates that the SWT structure is stable and can effectively block the elongation of T7 RNAP, thereby strongly repressing downstream gene expression. The ON/OFF ratio refers to the fluorescence activation signal in the presence of trigger RNA (ON state) divided by the leakage fluorescence signal in the absence of trigger RNA (OFF state). A high ON/OFF ratio indicates that SWT can significantly enhance downstream gene expression in the ON state, demonstrating strong activation capability. In this study, the fluorescence signals of 21 algorithm-generated SWT variants (The secondary structure predictions are shown in Figure S3) were measured under two different states, with both SWT sensor and trigger concentrations set at 10 nM. The fluorescence in the OFF state reflects the transcription termination efficiency of these 21 SWTs. Among them, JS5, JS6, JS8, JS9, and ZS17 exhibited high leakage fluorescence, indicating that their ability to inhibit T7 RNAP was weak and ineffective in blocking downstream gene transcription. In contrast, JS2, ZS1, ZS6, ZS19, and ZS22 exhibited minimal leakage expression, suggesting that the nucleotide distribution in these five SWT sequences maintained stable stem-loop structures, leading to effective transcriptional repression (Figure S4A). The fluorescence in the ON state reflects the transcriptional activation upon the addition of the complementary trigger RNA. Compared to the OFF state, JS2, ZS1, ZS16, ZS19, and ZS22 displayed a significant fluorescence signal output after adding the homologous trigger RNA, indicating that the base-pairing between sensor RNA and trigger RNA effectively disrupted the stem-loop structure, allowing T7 RNAP to proceed with transcription and activate downstream gene expression (Figure S4B). Additionally, we calculated the ON/OFF ratio for these 21 SWT variants (Figure 2B). JS2, ZS1, ZS16, ZS19, and ZS22 exhibited high ON/OFF values, with fluorescence activation levels of 10.41-fold, 17.71-fold, 18.52-fold, 22.21-fold, and 6.47-fold, respectively, upon the addition of the homologous trigger RNA, demonstrating effective transcriptional regulation. Although ZS17 exhibited the highest activation fluorescence intensity, its high leakage expression resulted in a relatively low ON/OFF ratio. To further validate the in vivo transcriptional regulatory capabilities of these five high-performance SWT variants, recombinant plasmids were constructed (Figure 2C) and tested in E. coli BL21(DE3) (Figure 2D). A plasmid containing only the 3WJdB reporter gene was used as a positive control. When an SWT was inserted, the fluorescence signal in vivo significantly decreased, indicating that the SWT could repress transcription in E. coli (OFF state). Upon the presence of trigger RNA, transcription activation levels of ZS1, JS2, ZS16, ZS19, and ZS22 were 2.82-fold, 2.41-fold, 2.00-fold, 9.13-fold, and 5.89-fold, respectively. Since the intracellular environment is more complex than the in vitro transcription system, multiple influencing factors (e.g., growth variability, cellular exclusion mechanisms, RNA degradation) may cause discrepancies between in vitro and in vivo results. However, our findings confirm that these five SWT variants also exhibit significant transcriptional activation effects in E. coli. Next, we predicted and evaluated the orthogonality of the five high-performance SWT sequences. Using the NUPACK online software (Version 3.1.0), we simulated the orthogonality of these five SWT pairs by analyzing the equilibrium concentrations of complexes and base pairing in the test tube ensemble (with both trigger RNA and sensor RNA set to 10 nM). The simulation results indicated that the homologous trigger and sensor RNA pairs were mutually orthogonal (Figure S5). We then conducted in vitro transcription experiments to verify their orthogonality (Figure 2E, with the fold change of homologous groups set to 1). The experimental results were largely consistent with the software predictions, with the exception of ZS16 and ZS22, which exhibited slightly weaker orthogonality, suggesting that some crosstalk might exist between their non-homologous trigger and sensor RNAs. Nevertheless, the overall orthogonality was well maintained. To further investigate the regulatory mechanism of SWT and optimize its regulatory efficiency, we selected ZS1 as a representative and explored the effects of different concentrations of trigger RNA and sensor RNA on in vitro transcription. Meanwhile, to further investigate the regulatory mechanism of SWT and optimize its regulatory efficiency, we used ZS1 as an example to examine the effects of in vitro transcription under different concentrations of SWT trigger and sensor. Through experiments, we measured the Fold Change under conditions where the SWT sensor and trigger concentrations were 5 nM, 10 nM, and 20 nM, respectively. (Figure 2F). On one hand, an increase in trigger concentration at a fixed sensor concentration led to greater activation efficiency. When the ZS1 sensor concentration was 20 nM, the fold change was 3.18-fold (5 nM trigger), 9.21-fold (10 nM trigger), and 25-fold (20 nM trigger). This could be attributed to the higher trigger concentration enhancing its binding with the sensor, leading to a more extensive disruption of the sensor stem-loop structure. As a result, more T7 RNAP molecules can continue transcription, producing higher amounts of 3WJdB, which binds to DFHBI-1T and generates a stronger fluorescence signal, thereby enhancing activation efficiency. On the other hand, at a fixed trigger concentration, the activation fold change also increased with rising sensor concentrations. When the trigger concentration was 20 nM, the fold change reached 4.77-fold (5 nM sensor), 17.91-fold (10 nM sensor), and 25-fold (20 nM sensor). This may be due to the fact that at low sensor concentrations, some of the transcribed trigger molecules fully bind with the available sensor, leading to an excess of unbound triggers. This saturation state could increase the burden on the transcription system or introduce non-specific binding signals. As the sensor concentration increases, the efficiency of trigger utilization improves, further enhancing activation. These findings confirm our analysis, demonstrating that transcription efficiency positively correlates with both the sensor and trigger concentrations within a certain concentration range. 3.2. SWT Cascade Reaction Construction and Expansion To achieve stepwise signal processing and dynamic transmission, we constructed a cascade reaction loop in vitro using SWT, which enables the output signal of one level to serve as the input signal for the next level, facilitating signal transmission. Multiple layers are connected to drive several modules to work together for dynamic regulation. The construction of a multi-layer cascade regulation loop not only increases the functionality of biological circuits, enabling multi-level dynamic regulation that can simulate advanced biological processes, but it also amplifies the signal, reduces background noise, and stabilizes the signal output. Each layer can serve as a gatepoint, ensuring that the next level’s response is only triggered under specific signal conditions, thereby functioning as a layered decision-making mechanism. Previously, we constructed a 3-level cascade loop in vitro based on SWT. In this study, after confirming the orthogonality of the five SWT sequences (ZS1, JS2, ZS16, ZS19, and ZS22), we extended the cascade reaction loop to 6 levels. This was achieved by replacing the fluorescent reporter gene 3WJdB, the output module after the SWT, with the trigger RNA of the next level, thereby enabling multi-level regulation. The detailed construction strategy is shown in Figure 3A. The entire regulatory network is divided into three modules: the signal input module (X), the signal conversion module (Y), and the signal output module (Z). The trigger RNA ZT1 transcribed by the input module is responded to by the second-level signal conversion module, where it binds with ZS1-ZT16, disrupting the hairpin structure of ZS1, allowing downstream gene ZT16 to be successfully transcribed. This process continues with responses from subsequent levels until the final signal conversion module outputs ZT19. Finally, ZT19 is responded to by the signal output module ZS19-3WJdB, binding and disrupting the hairpin structure of ZS19, allowing the reporter gene 3WJdB to be successfully transcribed, producing and releasing fluorescence after binding to the fluorescent molecule. Due to the limitations of the raw materials in the in vitro transcription system, the DNA templates that can be transcribed are also limited. To reduce the load on the transcription system, we fixed the concentration of the signal conversion module at 5 nM for all four layers and characterized the signal input and output modules at different concentrations. We co-transcribed the six linear DNA templates of the entire cascade regulation model in the same transcription system (one-pot method). The results show that when the concentration of the signal input module X is 15 nM, the concentration of the conversion module Y is 5 nM, and the concentration of the signal output module Z is 5 nM, the cascade reaction exhibits a maximum fluorescence activation of 10.15 times (Figure 3B). Interestingly, we observed that when the concentration of the input module X was 15 nM, and the concentration of each signal conversion module Y was 5 nM, the final fluorescence output decreased as the concentration of the signal output module Z increased, showing a negative correlation. We hypothesize that this phenomenon may be due to two main factors. First, at high concentrations, the DNA template of the output module may competitively bind to T7 RNA polymerase, impeding the overall signal transduction process and leading to reduced fluorescence output. Second, the output module contains a structurally complex reporter gene, which is more prone to misfolding under high concentration conditions, thereby reducing the efficiency of fluorescent dye binding and leading to lower fluorescence signals. Additionally, as the total template concentration increases in the system, the transcription machinery becomes overburdened, and resources are depleted, which may further contribute to fluorescence signal degradation or transcriptional inhibition. Furthermore, we analyzed the real-time fluorescence under the optimal reaction conditions, and the entire cascade reaction was very rapid, reaching its peak in 30 min. Afterward, the fluorescence signal gradually faded due to the consumption of raw materials in the transcription system and crosstalk between templates (Figure S6A). To reduce crosstalk between templates and ensure complete transcription of the reporter gene, we also attempted to co-transcribe the signal input and conversion modules in the same system while transcribing the signal output module in a separate transcription system (Two-pots). Both systems were incubated at 37 °C for 30–45 min to accumulate a certain amount of RNA before being mixed into the same system to bind with the fluorescent molecule DFHBI-1T. The results showed that the real-time fluorescence no longer exhibited a decreasing trend, indicating that the transcription system limitation was improved to some extent. Still, the reaction process was very slow, with the maximum activation only reaching 1.26 times (Figure S6B,C). The effect was much weaker than the one-pot method, possibly because placing the entire cascade reaction in the same transcription system allows the signal transmission to be more synchronized, efficient, and precise, leading to a faster system response. 3.3. Construction and Characterization of the SWT Biological Logic Gate Biological logic gates introduce the concept of electronic logic gates and are functional elements and systems that perform logical operations in biological systems. These gates generate specific outputs based on input signals under certain conditions (e.g., concentration, enzyme activity, temperature, or other environmental factors). Previous research combined SWT with Toehold Switches to construct an AND gate that jointly regulates transcription and translation. Additionally, a two-input, three-layer OR gate was constructed by combining cascade reactions with the OR gate. In this study, we used a fluorescent aptamer as a reporter gene and constructed four new basic biological logic gate control circuits in vitro to achieve more flexible and precise control over the transcription process while expanding the logic operation capabilities of SWT. 3.3.1. AND Gate The AND gate is characterized by producing an output signal (output is 1) only when both input signals are present simultaneously (i.e., both inputs are 1). The construction of the AND gate involves combining two different SWTs to form two stem-loop structures that block transcription. Only when both homologous Trigger RNAs are present can these stem-loop structures be fully disrupted, allowing transcription to proceed normally (Figure 4A). To experimentally validate the functionality of this model, we assembled the AND gate by linking ZS22 and ZS19 SWTs together. We tested its performance through in vitro transcription (with all template concentrations set at 10 nM). The results showed that no significant fluorescence signal activation was observed when no input was present, only a single input of ZT22, and only a single input of ZT19. However, when both ZT22 and ZT19 were present as inputs, there was a 6.67-fold increase in fluorescence activation compared to the absence of any input (Figure 4B). This confirms the functionality of the AND gate model. 3.3.2. NOT Gate The NOT gate functions oppositely to the SWT, characterized by having signal output (output is 1) when no input signal is present (input is 0) and having no signal output (output is 0) when signal input is present (input is 1). The construction of the NOT gate model involves introducing a sequence (referred to as the “Anti-Toehold-Stem”, ATS) in front of the SWT that is complementary to the SWT’s toehold and upper stem regions, thereby disrupting the SWT structure. This causes the SWT to switch from a default open state (no fluorescence output) to a default closed state (fluorescence output), enabling signal output even without input. Subsequently, an input trigger RNA sequence (referred to as the “Anti-Anti-Toehold-Stem”, AATS) is introduced, which preferentially binds to the ATS sequence, allowing the previously disrupted SWT structure to recover and block transcription (Figure 5A). We constructed a NOT gate using ZS19 as an example and experimentally validated the functionality of the NOT gate model (where the concentration of input Trigger RNA template DNA was 20 nM, and the concentration of Sensor RNA template DNA was 10 nM). The results showed that when there was no input, a large fluorescence signal was output, while the fluorescence signal was significantly reduced when the input was present, indicating a switch from the default closed state to the open state, suppressing transcription and significantly reducing the fluorescence signal output to near zero (Figure 5B). 3.3.3. NAND Gate The NAND gate functions oppositely to the AND gate, being a combination of the AND gate and the NOT gate. Its characteristic is that, in the absence of input signals or with only one input signal, a fluorescence signal is output (output is 1); however, when both input signals are present simultaneously, no signal is output (output is 0). After validating the functionalities of the AND gate, NOT gate, and cascade reaction models, we combined their construction strategies and ideas to develop the NAND gate. The NAND gate regulatory circuit is essentially a three-layer cascade reaction. The first layer is the input module (input trigger RNA), the second layer is a signal conversion module controlled by an AND gate (sensor A-sensor B-AATS), and the third layer is a signal output module controlled by a NOT gate (ATS-sensor C-3WJdB). By replacing the reporter gene 3WJdB in the AND gate model with the AATS sequence, the system ensures that only when both trigger RNAs from the two SWTs (sensor A and sensor B) are present will the AATS sequence be transcribed. This sequence will then act as the input for the NOT gate, binding with the ATS sequence in the output module. When the first layer has no input or only one input, the second-layer conversion module will not transcribe the AATS sequence, preventing it from binding with the ATS sequence in the output module, thus allowing normal transcription. However, when both inputs (trigger A and trigger B) are present in the first layer, both hairpin structures (sensor A and sensor B) in the second layer will be destroyed, allowing the downstream gene AATS sequence to be transcribed. This sequence will serve as the input for the third-layer output module, binding with the ATS sequence, which restores the original conformation of the SWT (sensor C) secondary structure and blocks the transcription of the reporter gene 3WJdB, leading to a false output (Figure 6A). We used the ZS1, JS2, and ZS19 to validate the NAND gate model. The first layer consisted of the input modules ZT1 and JT2, the second layer was the signal conversion module ZS1-JS2-AATS19, and the third layer was the signal output module ATS19-ZS19-3WJdB. The input module template DNA concentration was 20 nM, while the template DNA concentrations of the signal conversion and output modules were both 10 nM. These three modules were co-transcribed for testing. The results showed that when there was no input, or when only ZT1 or JT2 was input, or when both ZT1 and JT2 were input, there was a significant fluorescence signal output (Figure S7). This did not align with the NAND gate logic, possibly due to template interference, complex secondary structures, or overloading in the transcription system. To address this, we improved the experimental method by transcribing the signal output module in a separate transcription system to ensure the reporter gene 3WJdB had enough energy and material to transcribe and fold properly without interference. The input and conversion modules were co-transcribed in another system. Both systems were incubated at 37 °C for 30–45 min to accumulate enough RNA, which was then mixed in the same system and tested for fluorescence with the fluorescent molecule DFHBI-1T. The results showed that when both ZT1 and JT2 were input, the fluorescence signal significantly decreased compared to conditions where no input, only ZT1, or only JT2 were used, which is more in line with NAND gate logic. However, when both input signals were simultaneously present, a substantial fluorescence signal was still observed, indicating that the system failed to realize the ideal “NAND” logic fully. We speculate that this may be due to insufficient concentrations of the Triggers or suboptimal binding kinetics with the Sensor, resulting in incomplete formation of the terminator structure and subsequent readthrough transcription. The simultaneous presence of two Triggers may also introduce new RNA-RNA folding pathways or intermediate conformations, thereby altering the original SWT termination mechanism and leading to unintended or partial activation. Additionally, in the in vitro transcription system, transcriptional resources such as RNA polymerase and NTPs may be competitively consumed, contributing to non-ideal fluorescence output (Figure 6B). 3.3.4. NOR Gate The NOR Gate is characterized by the absence of an output signal (output is 0) whenever any input signal is present. The design of the NOR gate is similarly inspired by the construction principles of the AND and NOT gates. By combining two NOT gates, the model ensures that no signal output is generated as long as either NOT gate is active (Figure 7A). We constructed the NOR gate using ZS19 and JS2, with the AATS sequences of ZS19 and JS2 serving as the two inputs. The DNA template concentrations were as follows: the signal output module ATS A (ZS19)-ZS19-AATS B (JS2)-JS2-3WJdB was set at 10 nM, while the input modules AATS A (ZS19) and AATS B (JS2) were set at 20 nM. Analysis of the results showed that a high fluorescence signal was observed in the absence of any input signals. However, when either AATS A (ZS19) or AATS B (JS2) was present, fluorescence significantly decreased, and when both were input simultaneously, the fluorescence signal was nearly zero (Figure 7B). These results indicate that the NOR gate model functions as expected. 3.4. Aptamer-Based Transcriptional Regulation Due to the high specificity between nucleic acid aptamers and their ligands and the conformational changes induced upon ligand binding that can disrupt downstream gene expression, nucleic acid aptamers can be developed as novel transcriptional regulatory elements. The thrombin aptamer is well-suited for constructing synthetic regulatory models due to its well-established research foundation, clearly defined structural characteristics [50], high specificity, and strong binding affinity. Moreover, thrombin is not endogenously expressed in cell-free systems or most non-mammalian biological systems, resulting in minimal background interference [51,52]. Therefore, in this study, we selected the thrombin aptamer as a functional regulatory element and inserted it into a transcriptional regulatory circuit to evaluate its potential for transcriptional control. Here, we selected four different thrombin aptamers and predicted their secondary structures (Figure S8A) and three-dimensional interactions with the thrombin protein (Figure S8B) using Mfold and AlphaFold3. Based on the reliability of the predictions, molecular docking visualization was performed for TBA25, TBA15, and TBA29. The results showed that TBA25, TBA15, and TBA29 interacted with the thrombin protein at 10, 3, and 2 nucleotide sites, respectively (Figure S8C), suggesting that TBA25 may have the highest binding affinity for its ligand. To experimentally characterize the binding affinities of these four candidate aptamers, we conducted circular dichroism (CD) spectroscopy analysis (Figure S9). The CD spectrum of TBA25 exhibited a global downward shift upon ligand binding, indicating that its conformation became significantly more relaxed. This structural change is expected to facilitate transcription activation. While TBA15, TBA29, and TBA61 are DNA aptamers, TBA25 is an RNA aptamer. To experimentally verify the regulatory effects of these aptamers on transcription, we performed in vitro transcription assays. First, to eliminate potential interference from the ligand itself, we confirmed that varying concentrations of thrombin had no significant impact on transcription (Figure S10). Next, we inserted the four aptamers at different positions upstream and downstream of the T7 promoter in linear DNA constructs, using 3WJdB linear DNA as the positive control. The transcriptional regulatory effects were analyzed by comparing transcription levels in the presence and absence of thrombin (800 nM). Real-time fluorescence measurements over 2 h (Figure 8A–G) showed that TBA25 exhibited the strongest transcriptional activation effect, with the highest fluorescence peak after 2 h. Furthermore, fold change analysis (Figure 8H) indicated that inserting the DNA aptamer at different positions upstream or downstream of the promoter had no significant effect on transcriptional regulation. This suggests that aptamer insertion at these sites has little impact on promoter function and does not interfere with the recruitment of T7 RNA polymerase while still exerting a certain degree of transcriptional activation. The RNA aptamer TBA25 exhibited the strongest transcriptional activation effect, enhancing transcription levels by up to 1.61-fold upon ligand binding. 3.5. Aptamer-SWT Transcriptional Co-Regulation To expand the flexibility and programmability of transcriptional regulation using SWT and aptamer, and to construct an integrated regulatory and sensing genetic element, we attempted to combine aptamer with SWT to explore their synergistic regulatory effects on transcription. Using ZS1 as an example, we also investigated the effect of inserting the aptamer at different positions upstream and downstream of the promoter and the synergistic transcriptional regulation of the two genetic elements (with the template concentration of the sensor RNA module being 10 nM, the trigger RNA template concentration being 20 nM, and the ligand molecule Thrombin concentration being 800 nM). Analysis of the transcription results showed that when TBA15 was combined with ZS1, inserting TBA15 at different positions upstream or downstream of the promoter resulted in varying transcription effects. Insertion of TBA15 downstream of the promoter led to high leakage expression, and the introduction of Trigger RNA (ZT1) could not activate transcription. However, when TBA15 was inserted upstream of the promoter, leakage expression was low, and the introduction of Trigger RNA (ZT1) resulted in a 4.83-fold fluorescence activation. This suggests that inserting TBA15 upstream of ZS1 interfered with the proper folding of the ZS1 structure, causing an increase in leakage expression and preventing response to the trigger RNA. When TBA29 was combined with ZS1, inserting it at different positions upstream and downstream of the promoter produced two distinct selective response results: when TBA29 was inserted upstream of the promoter, it responded only to the Trigger RNA but not to the Ligand; when TBA29 was inserted downstream of the promoter, it primarily responded to the Ligand, with a lower response to the Trigger RNA. The combination of the RNA aptamer TBA25 with ZS1 showed the best synergistic effect, exhibiting the lowest leakage expression and the highest ON/OFF value under all input conditions. Compared to inputs of just the Trigger or Ligand, the synergistic regulation resulted in a 3.3-fold and 7.84-fold increase in activation, respectively (Figure 9). In conclusion, we observed a synergistic regulatory effect between these two elements by combining aptamers and switchable transcription terminators (SWT) and comparing their transcriptional regulatory effects at different relative positions. Each element either promoted or inhibited transcription when acting alone, but when they acted together, they further enhanced transcription. 3.6. Construction of Transcriptional Regulation Models Based on Aptamer-SWT Integration To expand the response and integration of transcriptional regulatory circuits to input signals from different sources, achieve complex signal processing, and enhance the robustness and flexibility of regulatory circuits, this study combines aptamers and SWTs to construct biological logic gates and cascade reaction models with heterologous inputs. 3.6.1. Orthogonality Verification First, to verify that the aptamer and SWT function orthogonally as distinct genetic elements, ensuring their compatibility, modularity, and independence in synthetic biological circuit design and gene regulation, we selected TBA25 and ZS1—both of which demonstrated good performance—for orthogonality validation. The experimental conditions were set as follows: SWT and aptamer template DNA concentrations at 10 nM, trigger RNA template concentration at 20 nM, and ligand concentration at 800 nM. Figure 10A compares activated fluorescence peak values, while Figure 10B shows the percentage of activated fluorescence (with the activation fluorescence of homologous groups normalized to 1). The results indicate that the trigger and sensor of SWT exhibit absolute orthogonality, as the ligand molecule thrombin does not interfere with the sensor. However, a slight fluorescence activation was observed between the SWT trigger and the aptamer, possibly due to partial base complementarity between their sequences. Overall, the components of these two genetic elements exhibit good orthogonality. 3.6.2. Heterologous Input OR Gate Inspired by the previous exploration of the synergistic regulatory effects of the Aptamer-SWT system, the design concept for the heterologous input “OR” gate model involves placing the weaker regulatory aptamer upstream of the stronger regulatory SWT as a gating point, followed by the reporter gene 3WJdB. When the ligand molecule is introduced, the aptamer undergoes a conformational change upon specific binding with the ligand, which may disrupt the downstream SWT structure, making it more relaxed and allowing the expression of the downstream reporter gene, thereby producing a fluorescent signal. When Trigger RNA is introduced, it specifically binds to the Sensor RNA of SWT, disrupting its stem-loop structure and weakening the termination effect at the gating point, thus enabling the downstream reporter gene expression and fluorescence output. When both the ligand and Trigger RNA are introduced simultaneously, the activation effect is even more pronounced (Figure 11A). However, due to the different mechanisms of action between the aptamer and SWT, their regulatory effects also exhibit certain differences. To achieve “OR” gate logic operation, we reduced the input concentration of Trigger RNA to weaken SWT activation and balance the regulatory effects of the two elements. Experimental results (with Trigger RNA template concentration set at 10 nM, ligand molecule thrombin concentration at 800 nM, and response module concentration at 10 nM) showed that ligand input alone resulted in a 2.08-fold increase in fluorescence activation, Trigger RNA input alone led to a 2.11-fold increase, and co-input of ligand and Trigger RNA yielded a 3.02-fold increase in fluorescence activation (Figure 11B). These results confirm the validity of the “OR” gate model. 3.6.3. Heterologous Input AND Gate The construction of the heterologous input AND gate is achieved by using the aptamer and SWT to regulate the correct folding of the reporter gene, enabling the correct output for the AND gate. Inspired by the design of the OR gate, we inserted an aptamer combined with a hairpin structure as the first gating point upstream of the reporter gene to hinder T7 RNAP elongation. The hairpin structure also further reduces leakage expression. The SWT is placed downstream of the reporter gene as the second gating point, interfering with the correct folding of the reporter gene. When only the ligand is input, the SWT interferes with the proper folding of the reporter gene, so no significant fluorescence activation occurs. When only trigger RNA is input, although the reporter gene can fold correctly, the hairpin structure after the aptamer also obstructs transcription, resulting in no substantial fluorescence activation. Only when both signals are co-input is a large fluorescence signal activation (Figure 12A). We first tested the regulatory potential of four aptamers for constructing the AND gate and found that the combination of TBA25 and ZS1 yielded results more in line with AND gate logic (Figure S11A). Additionally, when using 3WJdB as the reporter gene, SWT did not effectively interfere with its normal folding, possibly because 3WJdB consists of three Broccoli aptamers connected by a trivalent junction point, making it structurally complex and stable, not easily disrupted (Figure S11B). Therefore, when constructing the AND gate regulatory model with nucleic acid aptamers and SWT, we replaced 3WJdB with the structurally simpler Broccoli aptamer, whose proper folding is more easily interfered with. The results of the AND gate model testing (with Trigger RNA template concentration at 1 nM, ligand molecule thrombin concentration at 800 nM, and response module concentration at 10 nM) showed that when either the ligand or trigger RNA was input alone, there was no significant fluorescence output. However, when both signals were co-input, there was a 2.98-fold fluorescence activation (Figure 12B). These results confirm the validity of the heterologous input AND gate model. 3.6.4. Construction of a Cascade Reaction Using Aptamer-SWT Integration We used SWT to construct multi-layer cascade reactions and have already validated the orthogonality between aptamer and SWT. To extend the input sources for cascade reactions and respond to more complex environmental signals, this study combines aptamers with SWT to build a cascade reaction. We used thrombin as the input module (X), connected the well-performing TBA25 verified previously with the input signal ZT19 to form the signal conversion module (Y), and connected ZS19 with 3WJdB to form the output module (Z). When the ligand thrombin is introduced, TBA25 binds to thrombin, inducing a conformational change that promotes the expression of downstream genes, producing more ZT19 and ultimately generating more activated fluorescence signals (Figure 13A). We also explored the transcriptional regulatory effects of each module under different concentration conditions. Due to limitations in the transcription system, we fixed the output module concentration at 10 nM and investigated the changes in fluorescence activation peak values at input module concentrations of 0 nM, 50 nM, 200 nM, and 800 nM, and signal conversion module concentrations of 5 nM, 10 nM, and 20 nM. The results showed that when the ligand concentration of the input module was 800 nM, the promotion of the output fluorescence signal was significant. The best promotion occurred when the input ligand concentration was 800 nM, the conversion module concentration was 20 nM, and the output module concentration was 10 nM, greatly enhancing the final fluorescence output. This further validates the functionality of the cascade reaction and demonstrates that the aptamer can indeed be used for the transcriptional regulation of downstream genes (Figure 13B).