iGEM 2022 — Team warwick

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{% block lead %}Describe how and why you chose your iGEM project.{% endblock %}
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#### Lab Book

### Lab Book 1

### Lab Book 2

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{% block title %}Engineering Success{% endblock %}
{% block lead %}Describe how and why you chose your iGEM project.{% endblock %}
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#### Engineering Success

### Contents

[Introduction](#Intro)

 [Optimising Aptamer Sensing System](#Optimisation)

 [Modelling](#model)

 [Test of Enzyme Activity](#Assay)

### Introduction

![Chart](https://static.igem.wiki/teams/4210/wiki/engineering-success/engineering-success-1.png)

**Figure 1. The Engineering Cycle**

To make our project more robust, we aimed to pass our builds through multiple design cycles based on experimental results, modelling input and guidance from stakeholders. Each engineering cycle would closely follow the iGEM engineering process, which involves deciding what we wanted our sensing system and *E. coli* to be capable of, assembling and testing its capabilities, before analysing the results to better inform the next cycle.

Due to unprecedented delays in shipping and handling, we had a smaller window to explore what could be done better. In an effort to refine our project to fit within the bounds of the competition, we were unable to put our project through the desired rigour. However, we were able to make some important decisions during the course of the project to increase the likelihood of success within the iGEM timeframe.

### Composite Geneblock with Repeater Sequence

During initial dry lab planning, we designed *Pyre1* with BsaI flanking sites to ensure compatibility with basic parts from the iGEM distribution kit. We ordered our gblocks from IDT, which was used during a one-pot Golden Gate cloning reaction into storage vector pJET. After low success with the assembly of the individual basic parts, we went back to the drawing board to consider alternatives. We concluded that there was little value in assembling the transcriptional unit in-house from individual parts. In the interest of time, we decided to design a composite part containing a promoter, RBS, Pyre1 and a terminator ([Parts Overview](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27part-overview%27%29%20%7D%7D)). At the same time, our initial protein simulations showed low confidence in terms of folding. As a precaution, we added a repeater sequence to Pyre1 to minimise unintended amino acid interaction between CarCB2 and the ice nucleation protein.

![Chart](https://static.igem.wiki/teams/4210/wiki/engineering-success/engineering-success-2.png)

**Figure 2. Two versions of our *Pyre1* fusion gene. *Pyre1* v2 was generated after a mid-project cycle review.**

With both a repeater and a linker sequence, we increased the likelihood of Pyre1 folding into its native conformation and its insertion into the membrane without creating any spatial hindrances. Apart from engineering the composite gene to include a repeater sequence, it was found that incubation at a lower temperature of 30°C also increased the likelihood of membrane insertion.

### Optimising Aptamer Sensing System

To ensure a complete aptamer-based biosensing system is devised for our target pesticide, it was essential that each step and reagent involved went through optimisation cycles to ensure an accurate, and highly efficient detection system during implementation. When we used the initial protocol from existing literature, it failed to produce results. Subsequently, we returned to the drawing board, breaking apart our protocol into its constitutive parts. and run multiple cycles of optimisation tests for each of the reagents involved, including gold nanoparticles (AuNP), PDDA, aptamers, and varying concentrations of the pesticide ([Results](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D)).

These sequential optimisation tests increased the likelihood of success with the implementation of our aptamer-based biosensor, especially within the iGEM timeframe. However, it is important to note that further optimisation tests are required, especially regarding increasing the sensitivities of the sensing system, so that pesticide levels much closer to the legal limit of lambda-cyhalothrin can be detected. This would be within the next engineering cycle. However, at present, this could not be conducted due to constraints of the iGEM timeframe.

### Modelling

The modelling team made recommendations to copy numbers and transcription rates based on growth data collected in the lab for further optimisation of colony degradation rates. This was due to well understood molecular biology trade-offs and a phenomenon known as “burden”. A brief overview of findings shows that increased transcription rates did not always lead to increased degradation due to negative impacts on growth rates. For more information and a greater insight into the modelling approach please see the [modelling](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%20%7D%7D) page.

### Test of Enzyme Activity

One of the biggest challenges throughout the project was designing a suitable enzyme assay to test for degradation of λ-cyhalothrin. This was primarily due to the chemical properties of both λ-cyhalothrin and 3-phenoxybenzaldehyde, the primary degradation product. These compounds are both hydrophobic and therefore have to be dissolved in organic solvents. This posed a challenge when used in living biological systems. Secondly, these difficulties were exacerbated by the time constraints imposed by delays in receiving the distribution kit and the timeline for obtaining successful transformation.

Initial tests were conducted using GC/MS, which failed to detect standards of our pesticide. Following discussions with Dr Lijiang Song (Head of Mass Spectrometry Facility), the susceptibility of GC/MS to water meant sample preparation would be difficult and the concentrations required were far too low to be confident in successful dilution of oils. Although this method is used in industry for pesticide screening in this use case it would require a large time investment to optimise and troubleshoot GC/MS results.

We later moved to working with TLC, designing protocols to screen multiple solvent systems (ethyl acetate, hexane and petroleum ether in varying ratios) as well as varying concentrations of λ-cyhalothrin and 3-phenoxybenzaldehyde to produce standards. Based on troubleshooting results we were able to visualise both compounds under UV at 254 nm. However, we discovered they held similar RF values, meaning it would be hard to distinguish mixed sample results from any enzyme assay.

Next, we tested with HPLC, this would be forgiving during our sample preparation, at the compromise of sensitivity. We were able to successfully detect both compounds in a methanol solvent, however we were not confident in the results due to errors between replicates. We discussed this with Christopher de Wolf (WISB Senior Technician), and redesigned our HPLC method changing solvent systems to acetonitrile with formic acid. Testing with this solvent system reduced the length of each run as the retention times were lower, saving us valuable time. We also moved to working over a larger range of concentration using standard dilutions to improve the reliability of our [standard curves](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D). We successfully developed standard curves for both compounds and therefore could set up an enzyme assay within this concentration range.

Despite what we have done so far, the iteration process is far from complete and there is potential for major improvements in the product development cycle before we are confident in presenting it as a minimum viable product.

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#### Results

### Contents

[λ-Cyhalothrin Biosensor](#Aptamer1)

 [Optimisation of Biosensor](#Aptamer2)

 [Implementation of Biosensor](#Aptamer3)

 [Aptamer Conclusion](#Aptamer4)

 [Expression of Pyre1](#cell1)

 [Growth Curves](#cell2)

 [Standard Curves](#cell3)

 [Degradation Results](#cell4)

 [Final Remarks](#cell5)

### Aptamer-Based Biosensor for λ-Cyhalothrin

After altering and optimising the aptamer-based biosensor protocol from the literature for our target pyrethroid pesticide, λ-cyhalothrin, we were able to obtain data showing a functional sensing system ([Design](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27design%27%29%20%7D%7D)). This is evidenced by the varying colour change from red to blue, dependent on the concentration of pesticide present within the sensing system (Figure 1). Moreover, calculation of the absorbance ratio (650 nm/528 nm) quantifies and supports these visual changes, as higher ratios represent higher concentrations of λ-cyhalothrin (Figure 2). The gradual colour change and progressive increase in absorbance ratio are indicative of a well-established visual sensing system.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result1.png)

**Figure 1. A spectrum from red to blue with an increase in concentration of λ-cyhalothrin (mg/mL) (from left to right) present within the biosensor.** +ve: Positive control; -ve: Negative control.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result2.png)

**Figure 2. Increase in blue-to-red wavelength absorbance ratio with an increase in λ-cyhalothrin concentration.** The positive relationship between absorbance ratio and concentration corresponds to the degree of visual colour change observed.

### Optimisation of Biosensor Protocol

Our optimisation assays were standardised with a 10-minute 30°C incubation after all the reagents were mixed.

**Optimal Wavelength for Dispersed Gold Nanoparticles**
As our sensing system relies on the red-to-blue colour change produced by the aggregation of gold nanoparticles (AuNP), it is important to identify our AuNP’s optimal wavelength within the 2 colour spectrums before representative blue/red absorbance ratios can be worked out. Standard literature protocols utilise wavelengths at 520 nm and 650 nm to measure the absorbance of dispersed (red) and aggregated (blue) AuNP respectively.

Following discussions with Prof. Matthew Gibson, our AuNP specialist from the University of Warwick’s Chemistry department, we agreed it would be best to manually work out our own AuNP’s optimal wavelength. Taking the typical 520 nm red-spectrum wavelength in literature as our reference point, we measured the absorbance of dispersed AuNP at wavelengths from 510 nm to 540 nm at 2 nm intervals. We established an absorbance curve that evidently peaked at the 528 nm wavelength (Figure 3), which was used thereafter throughout the biosensor development as our reference red wavelength; this was vital when it came to calculating absorbance ratios.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result3.png)

**Figure 3. Absorbance readings of dispersed 30 nm diameter gold nanoparticles from 510 nm to 540 nm.** The 528 nm wavelength produced the highest absorbance reading of ~1.2.

**Optimisation of PDDA**
Poly(diallyldimethylammonium chloride) solution (PDDA) is an essential reagent to the biosensing system. In the absence of our target pesticide, λ-cyhalothrin, PDDA polymers are saturated with aptamer molecules, forming aptamer-PDDA complexes. This leaves no PDDA free to interact with the dispersed AuNPs , thus there is no reaction to induce AuNP aggregation, hence colour change. The sensing system stays red, indicating the absence of λ-cyhalothrin. In contrast, in the presence of λ-cyhalothrin, the pesticide binds to the aptamers, resulting in fewer aptamers available to form the aptamer-PDDA complex. The PDDA, which is normally bound to aptamers when the pesticide is absent, is now free to interact with dispersed AuNPs within the system, hence enabling the aggregation of the AuNP that produces the colour change to blue which can now be observed.

 Subsequently, it was vital to determine the optimal volume of PDDA to be added to the system, to minimise false positive and false negative results, hence maximise the accuracy of our biosensor. Adding too much PDDA will lead to AuNP aggregation even without pesticide present, while adding too little would result in an insensitive system. Developing from our primary reference literature, we investigated the level of colour change in our sensor from no PDDA uptil PDDA volume equals that of AuNP. The volume of AuNP was kept constant at 50 μL, while the volume of PDDA increased from 0-50 μL with 5 μL intervals. 500 mM MOPS buffer was added to make up 100µL, the total volume of our system. The absorbance ratio reached its peak at 35 µL PDDA volume and levelled off with additional PDDA, suggesting maximal aggregation of the AuNP at roughly 35 µL (Figure 4).

 We carried out the subsequent optimisation assays with 45 µL AuNP, scaling up the volume of AuNP by 10 µL from the edge of plateau (35 µL) (Figure 4) to ensure an aggregation would be observed when λ-cyhalothrin is present.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result4.png)

**Figure 4. Increasing AuNP aggregation with an increasing volume of PDDA.** Degree of red-to-blue colour change occurring is dependent on the volume of PDDA present. ~35 μL PDDA, which corresponds to the absorbance ratio peak of 0.75, indicating the optimal PDDA volume for the biosensing system.

**Aptamer Optimisation**
Sequences of specific aptamer oligonucleotides for our target pyrethroid pesticide, λ-cyhalothrin, were obtained from literature after extensive research. 7 aptamers of varying affinities were selected and ordered from IDT. The full part list can be accessed [here](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27part-overview%27%29%20%7D%7D).

We decided to start our first sets of experiments in determining the optimal aptamer-PDDA proportion using LCT-9 aptamer, which has a medium affinity for λ-cyhalothrin. The aptamer was diluted from the stock concentration of 100 μM to our testing concentration of 1 μM with TE 1x buffer.

In hopes of finding the ideal aptamer-PDDA ratio at which the aptamer in the system saturates the PDDA present to prevent the characteristic red-to-blue AuNP aggregation (i.e. the point at which the lowest absorbance ratio is attained), varying volumes of LCT-9, from 0-50 μL with 10 μL intervals, were added to the preliminary system containing 45 μL PDDA and 50 μL AuNP, with corresponding volumes of 500 mM MOPS buffer to make the sensor up to 100µL. Calculated absorbance ratios showed that there was a drop in the degree of AuNP aggregation when 10-30 μL LCT-9 was used, indicating the optimal volume of aptamer required is approximately this range (Figure 5).

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result5.png)

**Figure 5. Change in AuNP aggregation level dependent on the level of 1µM LCT-9 aptamer, from 0-50 μL in 10 μL intervals, in the system.** Drop in absorbance ratio dropping at ~20µL provides a guiding range for the optimal aptamer volume.

In order to determine a more precise volume of aptamer required to balance out the PDDA, we zoomed into the 0-25 µL range of 1 μM LCT-9 aptamer with a smaller interval of 5 µL. The results showed that whilst there was a drop in absorbance ratio at 15 μL of LCT-9, there were significant fluctuations in the calculated absorbance ratio (Figure 6). We suspected that there might be excess PDDA within the system causing the unwanted AuNP aggregation, which would explain these fluctuations.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result6.png)

**Figure 6. Fluctuations in absorbance ratios with increasing volumes of 1 μM LCT-9, 0-25 µL inn 5 µL intervals.**

In light of these findings, we repeated the assay with a refined system, in which the PDDA volume was decreased to 40 μL and the MOPS buffer volume increased to 10 μL in place of the reduced volume. Again, it was evident that at ~20 μL of aptamer, there was a dip in absorbance ratio(Figure 7). Further investigation into the 9-30 μL LCT-9 range with 3 μL intervals revealed that at ~15 μL the absorbance ratio is the lowest, supporting this volume as the optimal amount required to offset the PDDA in the sensor (Figure 8).

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result7.png)

**Figure 7. Decreasing levels of AuNP aggregation, dependent on the levels of LCT-9 aptamer in the system.** Experiment re-ran with a lower volume of PDDA (40 μL, alongside 10 μL 500 mM MOPS buffer).

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result8.png)

**Figure 8. Decreasing levels of AuNP aggregation dependent on increasing of 1μM LCT-9 aptamer, 9-30 μL LCT-9 range with 3 μL intervals, in the system.**

Before proceeding with 15 μL as the optimal aptamer volume for our biosensor, we thought it would be appropriate to test the effectiveness of the same LCT-9 aptamer, except at a higher concentration. Hence, the initial stock 100 μM LCT-9 aptamer was suspended in TE 1x buffer, now creating the desired concentration of 10 μM; the testing concentration has now increased by 10-fold. The previous aptamer volume optimisation experiments were repeated again, in which the new 10 μM LCT-9 aptamer was tested, from 0-20 μL in 2 μL intervals (Figure 9). Whilst experimental findings supported the optimal aptamer volume to be around 14-16 μL, significant changes to the absorbance ratios could now be observed, which was not seen when using a lower concentration of the aptamer.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result9.png)

**Figure 9. Decreasing levels of AuNP aggregation, dependent on levels of 10 μM LCT-9 aptamer in the system, 0-20 μL in 2 μL intervals.** Optimal volume of aptamer required for the system is deduced to be ~15 μL.

These rounds of trial and error have therefore enabled us to work out the optimal aptamer concentration (10 μM) and volume (15 μL) required to further develop our aptamer-based biosensor for λ-cyhalothrin.

Moreover, to ensure the interchangeability of the aptamers that are used within the development of this biosensing system, LCT-1 aptamer that is of a higher affinity to λ-cyhalothrin was used for the same aptamer optimisation experiments. Using 10 μM LCT-1, diluted from 100 μM stock with TE 1x buffer, aptamers of volumes varying from 0 μL to 20 μL with 2 μL intervals were plugged into the the system. It became apparent that the optimal volume of aptamer required for the development of our biosensing system was again ~15 μL (Figure 10). Therefore, we drew the conclusion that 15µL 10µM aptamer is the optimal aptamer amount sufficient for preventing AuNP aggregation and maintaining the red colour of our system.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result10.png)

**Figure 10. Decreasing levels of AuNP aggregation, dependent on levels of 10 μM LCT-1 aptamer, 0 μL to 20 μL with 2 μL intervals, in the system.** Optimal volume of aptamer required for the system can be deduced to be ~15 μL.

**Testing with λ-cyhalothrin Pyrethroid Pesticide**

 Now that the reagents have been optimised, our system can undergo testing with our target pyrethroid pesticide, λ-cyhalothrin.
 Setting 30 µL as the baseline volume of pesticide to be tested, we combined 40 μL PDDA, 10 µL 500 mM MOPS buffer, 15 μL aptamer of choice, 30 µL 10mg/mL λ-cyhalothrin, and 50 μL AuNP sequentially. This was followed by a 10-minute incubation at 30°C.

We conducted the test with both LCT-1 and LCT-9 aptamers, with positive (no aptamer) and negative (no pesticide) controls. Visual observations and calculated absorbance ratios showed consistent results, indicating a red-to-blue colour change, thereby indicating the AuNP aggregation and the presence of the pesticide. Absorbance ratios increased from 0.35 to 0.53 (LCT-1) and 0.67 (LCT-9) respectively (Figure 11). With the experimental data aligning with the expected results, it was evident that our biosensor is able to detect λ-cyhalothrin.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result11.png)

**Figure 11. Aptamer-based biosensing system (using 50 µL AuNP) accurately indicating the presence of λ-cyhalothrin** LCT-9: Sensing system with LCT-9 and λ-cyhalothrin; LCT-1: Sensing system with LCT-1 aptamer and λ-cyhalothrin; No pesticide: Negative control; No aptamer: Positive control.

Whilst both our qualitative and quantitative data supported the presence of λ-cyhalothrin, the actual colour change was not easily observable without the aid of a white background, due to the mild colour of the sensor. To increase the colour intensity of the outcome, the previous experiment (of Figure 11) was rerun with 60 μL AuNP. We were able to reproduce the results of the experiment (Figure 12), but with a more discernible colour change (Figure 13).

As a result, we made the final refinement to our sensing system, standardising the system with:

* 40 μL PDDA
* 10 µL 500 mM MOPS buffer
* 15 μL aptamer of choice
* 30 µL pesticide with a concentration of choice
* 60 μL AuNP

Added from top to bottom of the list, followed by a 10-minute incubation at 30°C.
All further assays were carried out using this protocol.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result12.png)

**Figure 12. Aptamer-based biosensing system (using 60 µL AuNP) accurately establishes the presence of λ-cyhalothrin.** LCT-9: Sensing system with LCT-9 and λ-cyhalothrin; LCT-1: Sensing system with LCT-1 aptamer and λ-cyhalothrin; No pesticide: Negative control; No aptamer: Positive control.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result13.png)

**Figure 13. Observable red to blue colour change indicating the presence of λ-cyhalothrin.** Total volume of AuNP = 60 μL. +ve: Positive control (total AuNP aggregation); -ve: Negative control (no AuNP aggregation); LCT-1: Sensing system and λ-cyhalothrin; LCT-9: Sensing system with LCT-9 and λ-cyhalothrin.

Even though both aptamers used thus far (LCT-1 and LCT-9) enable a colour change from red to blue within the biosensing system in the presence of λ-cyhalothrin, the LCT-9 sensor showed a remarkably greater colour change than LCT-1, when the only difference in the assays was the choice of aptamers in the sensor. This did not follow what we were expecting, as the primary reference literature suggested that LCT-1 has a higher affinity for λ-cyhalothrin than LCT-9, meaning theoretically there should be stronger aptamer-pesticide binding for the LCT-1, hence more free PDDA to interact with AuNP in the system to give higher absorbance ratios.

 This led to us questioning the affinities of our ordered aptamers, which is why we ran an experiment to test the sensitivity of all the aptamers we had on hand with 10 mg/mL λ-cyhalothrin. Our own experimental data provided evidence for LCT-3 to be the most sensitive aptamer in detecting the presence of λ-cyhalothrin, giving the highest absorbance ratio of ~0.75. In contrast, LCT-1 had the lowest absorbance ratio of ~0.60 (Figure 14). This was further evidenced by visual observations, in which LCT-3 displayed the highest degree of red-to-blue colour change, while aptamers provided only a partial colour change from red to purple (Figure 15).

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result14.png)

**Figure 14. Sensitivity of sensing systems with different aptamers for λ-cyhalothrin.**  All tested aptamers displayed a red-to-blue colour change, with LCT-3 and LCT-1 sensors being the most and least specific respectively. No aptamer: Positive control; No pesticide: Negative control.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result15.png)

**Figure 15. Visual results of the sensitivity assay of biosensing system with different λ-cyhalothrin aptamers; samples with brighter blue hue indicate a more sensitive aptamer/ sensing system to λ-cyhalothrin.** All biosensors with distinct aptamers showed a certain degree of colour change from red to blue in the presence of λ-cyhalothrin, with LCT-3 sensor showing the most intense colour change, whilst LCT-1 sensor showing the weakest colour change. +ve: Positive control; -ve: Negative control.

After establishing the sensitivities of our aptamer sequences, experiments involving the detection of the the target pesticide at varying concentrations using differing affinity aptamers can subsequently be conducted.

### Implementation of Aptamer-Based Biosensor for λ-Cyhalothrin

As it has been established that LCT-3 is the aptamer enabling the most intense colour change in the sensing system, experiments to determine the sensing limit of our system were conducted using LCT-3 aptamer. Varying concentrations of λ-cyhalothrin from 1- 5mg/mL, in 0.5mg/mL intervals, were made from 10mg/mL λ-cyhalothrin stock by dilution in acetone.

The results once again showed that our optimised aptamer-based biosensing system is functional and capable of detecting λ-cyhalothrin (Figure 1 & 2). Closer analysis indicates that intensity of the red-to-blue colour change is dependent on the concentration of the λ-cyhalothrin pesticide present within the sensing system, as expected. From 0-3mg/mL λ-cyhalothrin, the absorbance ratio rose by 0.24 from ~0.29 to ~0.53, and steadily increased to ~0.62 at 5mg/mL. The positive relationship between degree of colour change and pesticide concentration is further evident when comparing the change in absorbance ratios with every unit of pesticide increased, with a significantly greater degree of AuNP aggregation observed at pesticide level above ~3mg/mL. Moreover, it is evident that maximal aggregation occurs at 10mg/mL of λ-cyhalothrin, as the absorbance ratio peaks at ~0.77; a similar absorbance ratio to the positive control ran at ~0.70.

We thought it would be best to further test the least sensitive aptamer that we had concluded from our previous experiments, LCT-1. Again, the same λ-cyhalothrin concentrations from 1-5mg/mL in 0.5mg/mL intervals were used. Whilst the overall trend showed that higher the concentration of the pesticide present within the sensing system, the higher the absorbance ratio, thereby giving a stronger indication of the pesticide presence, significant fluctuations in the ratios were seen, especially around ~0.30-0.55 (Figure 16). Furthermore, whilst a high absorbance ratio recorded for 10mg/mL of λ-cyhalothrin, it was only at ~0.60 that the reading was significantly lower than that of the positive control (~0.70) and LCT-3 sensor (~0.77).

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result16.png)

**Figure 16. Significant fluctuations seen in absorbance ratios of biosensor using λ-cyhalothrin aptamer of low affinity (LCT-1)**

Moreover, as we were also adopting this aptamer-based biosensing system for another pesticide – organophosphate fenitrothion, as part of our [partnership with Concordia University](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27partnership%27%29%20%7D%7D), we thought it would be beneficial to test the specificity of sensors with λ-cyhalothrin (LCT-3) and a fenitrothion (FenA2) aptamers in parallel. For this, we added 5 mg/mL λ-cyhalothrin to each of the sensors. Our results showed that only the LCT-3 sensor had a red-to-blue colour change (Figure 17), evidencing FenA2, which is not a complementary oligonucleotide sequence for λ-cyhalothrin, is not able to bind the pesticide within the sensing system to allow AuNP aggregation. This demonstrates the specificity of our aptamer-based biosensing system, and highlights the importance of using an aptamer that is a complementary to the pesticide targets within the aptamer-based biosensing system.

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result17.png)

**Figure 17. λ-cyhalothrin specificity test for biosensing systems with LCT-3 and FenA2 aptamers respectively.** Negative controls: FenA2 + no pesticide and LCT-3 + no pesticide; Positive control: no aptamer.

### Conclusion

After thorough optimisation tests involving the reagents, regarding volumes and concentrations, involved for the development of our aptamer-based biosensing system, we were able to develop a fundamental protocol which enables the sensing of our target pyrethroid pesticide, λ-cyhalothrin. Furthermore, it is evident that this system has the potential to be adopted for various other pesticide targets, as established by the successful adaptation of our λ-cyhalothrin sensing system for fenitrothion.

Whilst we have established a working sensing system for our target pyrethroid pesticide, λ-cyhalothrin, further tests involving producing a more sensitive sensing system, in which it is able to test for the pesticide at just above the legal limits (i.e., in parts per billion) need to be conducted. It is evident that the aptamers we have on hand may not be as sensitive enough, as the highest affinity aptamer (LCT-3) is not able to sense λ-cyhalothrin at 1 mg/mL. Therefore, instead of relying upon aptamer sequences in literature, it would be beneficial to specifically select aptamers for λ-cyhalothrin, via SELEX (Sequential Evolution of Ligands by Exponential Enrichment). Whilst further optimisation tests will be required, it will hopefully enable a more sensitive aptamer-based biosensing system to be implemented.

Furthermore, this opens the opportunity for our biosensor to be implemented for other pesticides, as aptamer sequences can be utilised via SELEX, as it is specific to the target compound), thereby highlighting the adaptability of our sensing system, and its prospects as an integrated in-field testing system ([Proposed Implementation](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27implementation%27%29%20%7D%7D)).

### Cell-Surface Expression of Carboxylesterase CarCB2 on *E. coli*

**Pyre1-expressing BL21 *E. coli***
Based on the workflow outlined in [design](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27design%27%29%20%7D%7D), we were able to generate Pyre1-expressing BL21 *E. coli* by transformation and screening on chloramphenicol LB agar (Figure 18). Based on NanoDrop readings, the miniprep products were 82-163 ng/uL ([Lab book](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27notebook%27%29%20%7D%7D))

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result18.png)

**Figure 18. Screening agar plate with chloramphenicol.** White colonies are successful pYRE001 transfects; green colonies are pYTK001 transfects which do not contain Pyre1

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result19.png)

**Figure 19. Gel electrophoresis picture of miniprep products digested with Xmn1.** White arrows: ~2300 bp (top) and ~800 (bottom) respectively.

Following our screening, we conducted gel electrophoresis with restriction enzymes on miniprep products that specifically cut within Pyre1. Bands at the expected length were produced, confirming that the colonies contained Pyre1 (Figure 19).

The miniprep products from 2 single colonies were then sent for Sanger sequencing, using a forward and reverse primer flanking the gene block. The sequencing results were analysed using SnapGene which unfortunately found a nonsense mutation for one of the colonies. The second colony, however, showed no mutations and was therefore successfully expressing Pyre1.

### **Effect of protein expression on growth**

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result20.png)

**Figure 20. A) Bacterial growth curve for Pyre1 at 30°C in LB broth.** Circles: BL21 (pYTK001); Squares: BL21 (pYRE001) **B) The log of absorbance at 600nm is plotted against time.** A trendline is plotted for the exponential growth phase between t = 60-160 mins and t = 60-180 mins respectively.

Figure 20 shows the bacterial growth curves; the lag phase is observed from t=0 to t=40. The exponential phase is from minute 60 to 160 (pYTK001) and from 60 to 180 (pYRE001), and is used to plot a trendline. This line had high R2 values of 0.9964 and 0.9944, showing the strong linear exponential growth. This range of values is used in the calculation of the growth rate and doubling time, as follows:
**Growth rate:**
The growth rate can be calculated from the Lotka growth equation Xt =X0eμt, which is rearranged for specific growth rate:
ln(Xt / X0) = μt
ln(0.692 / 0.175) / 1.334
μ = 1.031 h-1 (pYTK001)

ln(Xt / X0) = μt
ln(0.327 / 0.088) / 1.667
μ = 0.787 h-1 (pYRE001)

**Doubling time:**
The doubling time is calculated as follows:
Td= ln2 / μ
Td = ln2 / (1.031 /60) (conversion of specific growth rate to min-1)
Td = 40.34 minutes (pYTK001)

Td= ln2 / μ
Td = ln2 / (0.787 /60)
Td = 52.84 minutes (pYRE001)

Therefore, a 12.5 minute increase in doubling time was observed. This large increase can be explained through two main factors. Firstly, extra burden of protein expression on the *E. coli* chassis, which ultimately has a finite energy level and proteome mass. Secondly, membrane integration results in displacement of normal cellular transport channels, which reduces the amount of substrate the cell can import, thereby resulting in slower growth. This data was used to calibrate our model, which better explains cellular tradeoffs and lipid considerations. Find out more on our [modelling](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%20%7D%7D) page!

### **HPLC Standard Curves**

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result21.png)

**Figure 21. (A) Standard curve for 3-Phenoxybenzaldehyde concentration (B) Standard curve for λ-Cyhalothrin concentration.** Both graphs are generated from serial dilutions from 10 mg/ml to 0.625 mg/ml.

Figure 21 shows the standard curves derived from HPLC data analysis. Peak area was integrated from retention at 7.311 minutes for 3-phenoxybenzaldehyde (3-PBA) and 13.140 minutes for λ-cyhalothrin. The trendlines had R2 values of 0.8498 and 0.9736 respectively. The raw data from HPLC is included in the Appendix.

This standard curve would allow us to correlate concentration post enzyme assay to the peak area seen on HPLC. However, as the peak areas are already relatively low at concentrations around 1 mg/mL, this inevitably means we have to use concentrations higher than legal limits during our enzyme assay. This is further detailed in [Engineering Success](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27engineering%27%29%20%7D%7D) and
[Design](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27design%27%29%20%7D%7D).

### **Degradation Assay Results**

![Chart](https://static.igem.wiki/teams/4210/wiki/results/result22.png)

**Figure 22. A) HPLC graphs of samples where solvent was extracted from the supernatant.** Graphs 1 and 2 represent Pyre1-expressing strain with 0 mg/mL and 1.25 mg/mL of λ-cyhalothrin respectively. Graphs 3 and 4 represent pYTK001 strain with 0 mg/mL and 1.25 mg/mL of λ-cyhalothrin respectively. **B) HPLC graphs of samples where solvent was extracted from the pellet.** Graphs 1 and 2 represent Pyre1-expressing strain with 0 mg/mL and 1.25 mg/mL of λ-cyhalothrin respectively. Graphs 3 and 4 represent pYTK001 strain with 0 mg/mL and 1.25 mg/mL of λ-cyhalothrin respectively.

Figure 22 represents the results from the enzyme assay described in Design. We were unfortunately unable to detect the pesticide or degradation product in any sample (including the wild types) as there were no clear peaks at the expected retention times. This means we were unable to successfully extract the pesticide residue from the cell culture. Therefore, we were unable to elucidate the degradation capability of our enzyme. We expect this loss could occur at a couple of stages:

* After the addition of acetonitrile to the cell culture, the compounds could have been resistant to resuspension
* Following the vacuum evaporation of LB and addition of acetonitrile, a gel-like substance was formed. We could not verify what this could be, but this structure may have carried our compounds
* Alternatively, the compounds could be present, but were being overshadowed by the more inconsistent background peaks of the cell culture

Overall, although we were not able to successfully test the degradation ability of our enzyme due to time constraints, we were still able to successfully express the protein in the cell. This enzyme assay has potential to be re-run, with a few adjustments:

* Use of higher initial concentrations of λ-cyhalothrin may mean the peaks are less likely to be overshadowed by background in HPLC
* We would also troubleshoot different extraction methods, potentially with other solvents such as methanol
* Attempt to better understand the formation of the gel-like white structures during stages of our extraction

### **Final Remarks**

We set off on our iGEM journey full of ambition and hope, in an attempt to have a holistic system with real potential of solving a real world problem. At the end of the project we were proud to have great preliminary results. We successfully developed a novel aptamer based biosensor which provides rapid colour change at a fraction of the price of GC/MS. This system was also adapted to a second target with only minor adjustments. Our degradation project also has good preliminary results, we were able to successfully clone and transform our construct, analyse the effects on growth, and create standard curves for future use. We even made our first attempt at testing for degradation. Though unsuccessful, we were able to identify small changes which could translate to great results. We are very pleased with the time we spent in the lab, and proving the incredible prospect synthetic biology has, with its potential to change the world within a few short months!

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