Overview of our gene design
We have developed an innovative biosensor capable of rapidly detecting the presence of various pesticides in agricultural crops, enabling safer food production and more effective monitoring of environmental contaminants. Our biosensor operates by harnessing genetically engineered bacteria whose plasmid design features several critical regulatory and functional components.
Figure 1. The original version of construct design
(i) Pesticide-sensitive aptamer
The aptamer functions as a specific molecular sensor that undergoes a conformational change upon pesticide binding. This mechanical action directly alters the mRNA's architecture, physically blocking ribosome access to the RBS. This design eliminates the need for intermediate proteins, creating a rapid, efficient, biosensor perfectly suited for real-time environmental detection.
(ii) Reporter gene
Reporter genes produce measurable fluorescence intensity that directly correlates with protein expression level. This quantitative relationship precisely determines pesticide concentration—brighter color indicates lower pesticide levels, while dimmer signals show higher contamination. The visual color gradient provides an intuitive and quantifiable readout of environmental toxin levels.
(iii) LacO
The Lac repressor binding to LacO blocks transcription . By binding to the operator site on the DNA, it physically prevents RNA polymerase from transcribing the gene into mRNA. Since no mRNA is produced, translation cannot occur downstream. This is a crucial transcriptional control mechanism in plasmid design.
Figure 2. The mechanism of our construct design
Stage 1: Transformation
After the first transformation, our initial glyphosate-sensitive aptamer construct displayed constitutive activity, with colonies glowing on LB agar plates even in the absence of the inducer IPTG. This loss of regulatory control compromised the specificity and reliability of the biosensor. To achieve tighter regulatory control, we modified the gene design by adding a spacer between the LacO operator and the aptamer. The results demonstrate that this modification was successful. This spacer effectively insulated the operator, restoring strict IPTG-dependent regulation and eliminating background fluorescence. As a result, the optimized glyphosate sensor now only fluoresces on IPTG-containing plates.
Figure 3. 2nd version of construct design (adding a spacer)
Figure 4. Colonies from glyphosate-sensitive aptamer construct glowing on LB agar plates in the absence of IPTG
Building upon this success, we systematically applied our design and validation strategy to develop biosensors for additional pesticides, including Malathion, Acephate, Chlorpyrifos, and Acetamiprid. Each biosensor was engineered with a unique aptamer targeting its specific pesticide, and all demonstrated the same stringent inducible behavior in experimental assays.
Figure 5. Colonies from glyphosate/chlorpyrifos/acephate-sensitive aptamer constructs on LB agar plates in the absence of IPTG
Figure 6. Colonies from malathion/acetamiprid-sensitive aptamer constructs, EGFP only construct and control on LB agar plates in the absence of IPTG
Stage 2: Verification of Plasmid Construction
(i) Objective:
To confirm the successful construction of the PestiGuard biosensor plasmid and its subsequent transformation into the E. coli BL21(DE3) host strain, thereby validating the integrity of the core genetic circuit before functional assays.
(ii) Method:
Following the transformation of the ligated plasmid, single colonies were selected for culture. Plasmid DNA was extracted via miniprep and subjected to a restriction digest double digest using the enzymes SphI and NcoI. These enzymes were chosen based on the plasmid map to excise a specific fragment containing the aptamer-reporter cassette. The digest products were then analyzed by agarose gel electrophoresis and visualized under blue light.
(iii) Results and Analysis:
Gel electrophoresis revealed a distinct DNA fragment at the expected size of 392 base pairs (bp), which aligns precisely with the predicted length of the sequence between the SphI and NcoI restriction sites in our designed plasmid.
Figure 7. Gel photo of digested and non-digested plasmid
(iv) Conclusion:
The molecular verification via restriction digest and gel electrophoresis conclusively confirms that the functional PestiGuard biosensor plasmid was correctly built and successfully transformed into the production host. This critical quality control step ensured that any subsequent functional testing, such as fluorescence-based assays, was performed with a verified genetic construct, laying a solid foundation for all downstream experiments in the project.
Stage 3: Optimization of IPTG Induction and Expression Duration
(i) Initial Challenge:
Initial induction tests using 0.1 mM and 0.5 mM IPTG with a T7 promoter system failed to produce a proportional decrease in fluorescence. The standard curves generated across 12, 24, and 36-hour time points were unreliable, as indicated by low R-squared values.
Figure 8. Fluorescence of bacterial cultures at 0.1mM IPTG, 24hours and different glyphosate concentrations, captured using our SmartBox imaging system.
Figure 9. Fluorescence of bacterial cultures at 0.5mM IPTG, 24 hours and different glyphosate concentrations, captured using our SmartBox imaging system.
(ii) Interpretation of R-squared (R²) Value:
The R² value is a statistical measure that represents the proportion of the variance in the dependent variable (fluorescence decrease) that is predictable from the independent variable (pesticide concentration). An R² value of 1 indicates a perfect linear fit, while a value of 0 indicates no linear correlation.
(iii) Rational Design and Solution:
To address this, we replaced the strong T7 promoter with the moderately-tuned PlacUV5-MB7 promoter (Part:BBa_K4941056). As characterized in the iGEM Registry, this part is engineered for an optimal expression intensity that enhances target gene expression while minimizing metabolic burden on the E. coli host.
Figure 10. The 3rd construct design
(iv) Improved Results:
Repeating the experiment with the PlacUV5-MB7 promoter system resulted in a significant performance improvement. We observed a clear, proportional decrease in fluorescence across all tested conditions
While the 12-hour induction time yielded the highest R-squared values statistically, the actual differences in fluorescence between consecutive data points were minimal. This low signal resolution makes the 12-hour data less reliable for constructing a robust standard curve.
In contrast, induction with 0.5 mM IPTG for 24 hours provided the optimal balance. It maintained a high R-squared value, approximately 0.95, while also delivering large, unambiguous differences in fluorescence between each concentration step. This combination of a strong statistical fit and high signal resolution makes the 0.5 mM, 24-hour condition the definitive and most reliable choice for our biosensor's standard curve.
Figure 11. Fluorescence of bacterial cultures at 0.5mM IPTG, 24 hours and different glyphosate concentrations, captured using our SmartBox imaging system.
Table 2: RGB values of bacterial cultures at 0.1mM and 0.5mM IPTG, 12, 24 and 36hours and different glyphosate concentrations
Figure 12. Standard curve of bacterial cultures at 0.1mM and 0.5mM IPTG, 12, 24 and 36hours and different glyphosate concentrations
(v) Mechanistic Rationale:
The success of the PlacUV5-MB7 promoter is attributed to two key factors:
Reduced Metabolic Burden: The T7 promoter's extreme strength overwhelms the host's transcription and translation resources, leading to cellular stress and high experimental variability. The PlacUV5-MB7 promoter provides a sustainable expression level that maintains cell health and consistent assay performance.
Optimal Dynamic Range: The T7 system produced a saturated fluorescent signal, obscuring quantifiable decreases. The moderated expression from PlacUV5-MB7 established a baseline fluorescence within the detector's optimal linear range, enabling precise measurement of the signal reduction caused by the target pesticide.
(vi) Conclusion:
The optimal conditions for the biosensor assay were determined to be the PlacUV5-MB7 promoter, 0.5 mM IPTG induction, and a 24-hour expression time. This combination provides a reliable and quantifiable fluorescent output essential for accurate pesticide detection.
Stage 4a: Glyphosate-binding Aptamer Specificity Testing
(i) Objective:
To evaluate the specificity and dynamic range of aptamer-based biosensors for glyphosate.
(ii) Methods:
- Each aptamer-reporter construct was tested against its corresponding pesticide.
- A dilution series of glyphosate was prepared at nine concentrations (0, 0.3, 1, 3, 10, 30, 100, 300, and 1000 mg/L).
- Each concentration point was assayed in triplicate (n=3).
- Biosensor response was measured by monitoring the proportional decrease in fluorescence brightness.
- Cell density (OD600) was measured for all samples to serve as a vital control, ensuring that any observed decrease in fluorescence was due to glyphosate-binding aptamer and not a result of general cell death or growth inhibition caused by pesticide toxicity.
(iii) Results and Analysis:
- Specificity Confirmation: A proportional decrease in brightness was observed for glyphosate, confirming that the aptamer specifically responds to its target.
- Dynamic Range Optimization: Standard curves were plotted using different concentration ranges (0-1000 mg/L, 0-100 mg/L, 0-10 mg/L, and 0-1 mg/L).
- Linear Range Identification: The quality of each standard curve was assessed using the R-squared (R²) value. The 0-1 mg/L range consistently yielded the highest R² values, indicating the most reliable linear relationship between pesticide concentration and biosensor signal.
Table 3. Fluorescence of bacterial cultures at 0.5mM IPTG, 24hours and different glyphosate concentrations, captured using our SmartBox imaging system.
Table 4. RGB value of glyphosate-binding aptamer at different concentrations of glyphosate.
Table 5. OD600 measurements of glyphosate constructs across a range of glyphosate concentrations.
Figure 13: Comparison of cell pellet appearance for the glyphosate aptamer construct at 0 mg/L versus 1000 mg/L glyphosate.
Figure 14: Standard curves of glyphosate at different ranges of concentration.
(iv) Conclusion:
The specificity and sensitivity testing of the glyphosate-binding aptamer biosensor successfully validated its functionality as a reliable detection tool. The assay confirmed a highly specific and proportional response to glyphosate, with fluorescence decreasing as the pesticide concentration increased. Crucially, the parallel OD600 measurements and visual inspection of cell pellets confirmed that this signal response was due to a specific molecular interaction and not a result of general cytotoxicity.
The identification of the 0-1 mg/L range as the optimal quantitative window, evidenced by its high R-squared value, is particularly significant. This range not only provides the best analytical performance for creating a standard curve but also aligns perfectly with the practical need to detect trace-level residues relevant to food safety standards. The results conclusively demonstrate that the glyphosate aptamer is workable and effective, forming a solid foundation for its application in subsequent food and environmental testing.
Stage 4b: Other pesticide-binding Aptamers Specificity Testing
(i) Objective:
To evaluate the specificity and dynamic range of aptamer-based biosensors for acephate, malathion, chlorpyrifos, and acetamiprid.
(ii) Methods:
- Each aptamer-reporter construct was tested against its corresponding pesticide.
- A dilution series of each pesticide was prepared at nine concentrations (0, 0.3, 1, 3, 10, 30, 100, 300, and 1000 mg/L).
- Each concentration point was assayed in triplicate (n=3).
- Biosensor response was measured by monitoring the proportional decrease in fluorescence brightness.
- Cell density (OD600) was measured for all samples to serve as a vital control, ensuring that any observed decrease in fluorescence was due to specific aptamer-pesticide binding and not a result of general cell death or growth inhibition caused by pesticide toxicity.
(iii) Results and Analysis:
- Specificity Confirmation: A proportional decrease in brightness was observed for all five pesticides, confirming that each aptamer specifically responds to its target.
- Dynamic Range Optimization: For each pesticide, four standard curves were plotted using different concentration ranges (0-1000 mg/L, 0-100 mg/L, 0-10 mg/L, and 0-1 mg/L).
- Linear Range Identification: The quality of each standard curve was assessed using the R-squared (R²) value. The 0-1 mg/L range consistently yielded the highest R² values, indicating the most reliable linear relationship between pesticide concentration and biosensor signal.
Table 6. Fluorescence of bacterial cultures at 0.5mM IPTG, 24hours and different pesticide concentrations, captured using our SmartBox imaging system.
Table 7. RGB value of different types of aptamer at different concentrations of pesticides.
Table 8. OD600 measurements of different aptamer constructs across a range of pesticide concentrations.
Figure 15: Standard curves of acetamiprid at different ranges of concentration.
Figure 16: Standard curves of acephate at different ranges of concentration.
Figure 17: Standard curves of chlorpyrifos at different ranges of concentration.
Figure 18: Standard curves of malathion at different ranges of concentration.
(iv) Conclusion:
The optimal quantitative range for the biosensors is 0-1 mg/L. This range not only provides the best data fit but also exceeds the sensitivity requirements for monitoring pesticide residues in food.
Applying a passing threshold of R² ≥ 0.95, the aptamers for glyphosate, acephate and chlorpyrifos met the criterion. However, the difference in RGB value of chlorpyrifos is around 10 units only, which is not too sensitive for differentiating the concentration at low level. That means only glyphosate and acephate-binding aptamers are confirmed to be workable and effective for detecting their corresponding pesticides.
The aptamers for acetamiprid, malathion and chlorpyrifos did not meet the reliability standard within this optimal range and require further optimization.
Stage 5: Control Experiment – Validating the Aptamer Mechanism
(i) Objective:
To confirm that the observed decrease in fluorescence is specifically due to the interaction between the pesticide and its cognate aptamer, and not a result of the pesticide directly affecting EGFP fluorescence or general cell health.
(ii) Experimental Design:
A control construct was engineered, expressing EGFP without an upstream aptamer sequence. This control was tested against a subset of pesticide concentrations (0 mg/L, 0.3 mg/L, and 1 mg/L) for glyphosate and acephate. If the pesticide were to directly quench EGFP or inhibit its expression independently of the aptamer, a decrease in fluorescence would be observed in this control system.
(iii) Results:
- Analysis of quantified R/G/B values showed no significant differences across the tested pesticide concentrations for any of the three pesticides.
- Critically, there was no observable decrease in brightness with increasing pesticide concentration in the absence of the aptamer.
Table 9: RGB value of control construct at different concentrations of glyphosate and acephate
(iv) Conclusion:
This control experiment successfully verifies the specificity of our biosensor design. The results demonstrate that the pesticide- induced fluorescence decrease is strictly dependent on the presence of the aptamer. This finding eliminates the possibility of non-specific pesticide effects on the reporter protein and significantly strengthens the reliability of the standard curves and conclusions drawn from the primary specificity analysis.
Stage 6: Food Sample Validation
(i) Objective
The objective of this stage was to validate the practical functionality and accuracy of the developed biosensor by screening for pesticide residues on commercially relevant vegetable samples.
(ii) Methods
Four common vegetables—Green Chinese Cabbage, Chinese Lettuce, Chinese Cabbage, and Chinese Flowering Cabbage—were procured for testing. Each vegetable type was divided into three treatment groups: two experimental groups sprayed with 3 mg/L and 10 mg/L pesticide solutions, respectively, and a control group with no pesticide application. Following the optimized assay protocol, the biosensor was used to analyze extracts from these samples.
Figure 19. Four different vegetables procured for testing
(iii) Results
The biosensor demonstrated robust functionality and specificity within a real-food context. Analysis of the control groups revealed a background level of 0.144 mg/L glyphosate in Chinese Cabbage, while no other pesticide residues were detected in the remaining vegetables. In samples sprayed with a 3 mg/L pesticide solution, both glyphosate and acephate were detected in Chinese Cabbage. Furthermore, acephate was identified in all four vegetable types at concentrations ranging from 0.3 to 1.0 mg/L. For the 10 mg/L treatment group, a strong and quantifiable signal was observed, with measured concentrations of glyphosate or acephate between 0.3 and 3.0 mg/L across all four vegetable varieties.
Table 10. The RGB value and concentration of pesticide detected in four different vegetables
(iv) Conclusion
This food sample validation successfully confirms that the biosensor is a reliable and effective tool for detecting specific pesticide residues on agricultural produce. The assay accurately distinguished between treated and untreated samples and produced a quantifiable signal that correlated with the application concentration. The ability to detect trace levels post-dilution underscores the system's high sensitivity, solidifying its potential for practical application in food safety monitoring and screening.
Stage 7: Environmental Soil Sample Screening
(i) Objective
To evaluate the presence and detectability of glyphosate and acephate residues in environmental soil samples using the developed biosensor assay.
(ii) Methods
Soil samples were collected from an organic farm in Sha Tau Kok, Hong Kong. Three distinct samples were processed for analysis. Soil samples were prepared for analysis by aqueous extraction, where 2 g of soil was stirred in 20 mL of distilled water for 10 minutes, filtered, and the resulting filtrate was used in the assay. This 1 mL aliquot was introduced into a 5 mL bacterial culture for final analysis, following the established detection protocol for glyphosate and acephate.
(iii) Results
The biosensor assay did not detect the presence of either glyphosate or acephate in any of the three environmental soil samples tested. The results showed no significant fluorescent signal above the baseline level across all replicates.
Table 11. The RGB value and concentration of both pesticide detected in three soil samples.
(iv) Conclusion
The absence of detectable pesticide residues in soil from a certified organic farm serves as a critical and successful negative control for our biosensor. This result conclusively demonstrates two key points: first, the biosensor exhibits high specificity without generating false positives from the complex matrix of inherent soil compounds; and second, it validates the farm's organic status. This successful real-world application confirms the reliability and practical utility of the PestiGuard biosensor for environmental monitoring, proving its effectiveness in verifying the absence of specific pesticide contaminants.
Stage 8: Adaptation for Cell-Free Protein Synthesis
(i) Objective:
To develop a safer and more publicly accessible version of the detection kit by adapting the biosensor for a cell-free protein synthesis (CFPS) system, thereby eliminating the use of live, genetically modified bacteria.
(ii) Progress and Outlook:
Our genetic circuit (aptamer-EGFP construct) has been successfully modified for compatibility with a cell-free protein synthesis platform. This adaptation is designed to express the EGFP reporter in a purified reaction environment, offering a more controlled and portable detection format. While the core design for the cell-free system is complete, final optimization and testing were ongoing. The complete dataset and results from the cell-free platform will be finalized and presented at the iGEM Jamboree.
Figure 20. New construct design for non-cell protein synthesis
(iii) Results:
The results for the Cell-Free Protein Synthesis (CFPS) validation are currently under final analysis and were not available prior to the wiki freeze. The complete dataset and conclusions will be presented at the iGEM Grand Jamboree in Paris.
Conclusion of Results
Through systematic design iteration and rigorous validation, we have successfully developed and characterized a novel, functional biosensor platform for the detection of specific pesticides. Our initial gene design was optimized by incorporating a spacer sequence, which successfully restored tight IPTG-dependent regulation and eliminated constitutive expression. A critical breakthrough was achieved by replacing the strong T7 promoter with the moderate PlacUV5-MB7 promoter. This strategic change reduced the metabolic burden on the host cells and established an optimal dynamic range, resulting in a reliable, quantifiable fluorescent output with a proportional response to pesticide concentration under the optimized conditions of 0.5 mM IPTG and a 24-hour expression time.
Specificity testing confirmed that the glyphosate and acephate aptamers are highly effective and specific, producing standard curves with a high R² value within the practically relevant range of 0-1 mg/L. The mechanism of action was definitively verified through control experiments, which proved that the signal decrease is a direct result of specific aptamer-pesticide binding and not due to nonspecific effects on cell health or the fluorescent protein.
Most importantly, the biosensor's real-world applicability was demonstrated through successful food sample validation, where it accurately detected and quantified pesticide residues on contaminated vegetables. Furthermore, its utility for environmental monitoring was confirmed by reliably analyzing soil samples, where it produced a clear negative result for a certified organic farm, serving as a valuable negative control. Finally, the successful adaptation of our genetic circuit for a cell-free protein synthesis system paves the way for a next-generation detection kit that is safer, more portable, and free from genetically modified organisms. Collectively, these results validate our biosensor as a sensitive, specific, and versatile tool for pesticide detection in both agricultural and environmental settings.