Synthesis of Biomarkers via E.Coli
Figure 1: Outline of Biomarker Synthesis Experiments
Since we did not have permission to work with cell lines and our first year focused on theoretical experiments, we decided to use E.Coli as our model system (the human body) and have it synthesize our biomarkers of interest.
Then, with our synthesized biomarkers from E.Coli, we used our aptamer based detection method to detect the synthesized biomarkers, which allowed us to form a theoretical model for our constructed at-home detection system. We compared the detection of the E.Coli synthesized biomarkers to ordered biomarkers in order to investigate and account for any discrepancies that occurred.
We synthesized 27 composite parts, 9 for each biomarker(Mucin, Her-2, Mammaglobin B) we were investigating for the 2021 iGEM project. While it was possible to synthesize a standard construct with T7 promoter and terminator, we wanted to characterize the effectiveness of various parts from the iGEM registry in synthesizing biomarkers, which are often expensive to order.
We first selected the promoters we were interested in testing: T7 promoter(BBa_I719005), optimized (TA) repeat constitutive promoter(BBa_K137085) and Anderson promoter(BBa_J23100). We selected the T7 promoter and Anderson promoters as they are standard promoters that all iGEM teams use, and the optimized TA repeat constitutive promoter because literature has shown the presence of TATA and CCAAT boxes in the human promoter regions of our biomarkers of interest(Hurst, 2001). All our promoters are constitutive as our desire was to synthesize our biomarkers and not regulate any production.
We then selected the terminators we were interested in testing: T7 terminator(BBa_K731721), LuxICDABEG terminator(BBa_B0011) and T1 terminator(BBa_B0010). We selected these three terminators due to their high usage in past iGEM projects and compatibility with various constructs.
With 3 possible promoters and 3 possible terminators, in order to test optimal promoter terminator combinations, we came up with 9 possible combinations for each biomarker we wanted to synthesize. In addition to the promoters and terminators, our constructs consisted of the coding sequence of biomarker, GFP, and RBS. GFP was used such that we could use fluorescence as a qualitative indicator if our biomarker was successfully synthesized.
Figure 2: Design of biomarker producing parts
In order to test our parts, we first used PCR to amplify our protein coding sequence. The protein sequence for Mucin-1, Her-2, and Mammaglobin B were all found via the NCBI nucleotide database. We then selected our forward and reverse primers based on an extensive literature review we performed.
We selected the following primers for our biomarkers.
Forward primer CAACCAAGTGAGGCAGGTCC
Reverse primer GGTCTCCATTGTCTAGCACGG
(Samouëlian et al., 2018)
Forward primer GTGCCCCCTAGCAGTACCG
Reverse primer GACGTGCCCCTACAAGTTGG
(Samouëlian et al., 2018)
Forward Primer AAACTCCTGGAGGACATGGTT
Reverse Primer ACTGCTTGAATTTCCCCATAGC
(Zhang et al., 2020)
We then used Gibson Assembly to construct our part in the iGEM chloramphenicol backbone pSB1C3, before transforming it into E.Coli cells. Following transformation, we first qualitatively analyzed the plates to see if they were glowing. Since our construct was tagged with GFP, if the biomarker was successfully produced, then our colonies would glow.
Following transformation, we noticed that over half of our constructs were not glowing, indicating that our constructs were not successful. We hypothesized that rather than a construct design error, this was likely due to a transformation error as the positive controls for the transformation experiment showed growth. We repeated the experiment by precisely following the protocols for heat shock along with using the high efficiency transformation protocol with New England Biosciences. Repetition of transformation was successful, and we had all of our 27 constructs glowing. For teams that are conducting transformation, we highly recommend that heat shock duration is crucial for successful plasmid expression in the host cell.
Figure 3: Successful Transformation Colony
Figure 4: Unsuccessful Transformation Colony
Finally, while qualitative analysis would only let us know whether the biomarker was produced, we wanted to compare the amount of biomarker we produced from the different constructs. We used freezing and cracking accompanied by purification to purify our biomarker and quantify its yield via a 96-well plate reader. Since we did not attach a tag to our construct, freezing and cracking was used as our biomarker sequence was very large.
We used the 96-well plate reader and a Braford assay standard curve to quantify our protein yield between the different constructs. Our results demonstrated that we were able to successfully produce a biomarker from our construct and characterize the efficiency of different promoters and terminators in synthesizing our biomarkers.
Analysis of Protein Concentration
To calculate the concentrations of the biomarkers within each well, we first ran the BSA model to create the standard curve, where the x-axis represents absorbance, and y-axis represents concentration in mg/ml. The standard curve later allowed us to convert the measured absorbance into measurements of concentration. The equation and the image of the standard graph we derived from the BSA model is as follows.
Figure 5:Our line of best fit derived from polynomial regression is y = 0.1258x^3 - 0.5139x^2 + 0.848x + 0.2487. We chose to model our equation as a cubic function because it provided a better approximation for our data, with a correlation of 0.9607 and root mean square error (RMSE) of 0.0835 as seen above.
Using the standard curve and the measured absorbance, we interpolated values of concentration for each of the different constructs. We performed this conversion by substituting the absorbance values into the x-variable in our standard curve, to find the corresponding y-variable which represents concentration. The post-conversion concentrations of the biomarkers are listed below.
|T7 Promoter+T7 Terminator||Mucin(A1)||1.83|
|Anderson Promoter+T7 Terminator||Mucin(A2)||N/A|
|Optimized Promoter+T7 Terminator||Mucin(A3)||N/A|
|T7 Promoter+T1 Terminator||Mucin(A4)||0.16|
|Optimized Promoter+T1 Terminator||Mucin(A6)||0.16|
|T7 Promoter+Lux Terminator||Mucin(A7)||0.15|
|Optimized Promoter+Lux Terminator||Mucin(A9)||0.49|
For our project, the effectiveness of a construct is measured by the concentration of biomarkers produced. Thus, using the concentrations, we proceeded to compare them among each type of biomarker to see which of the constructs (1-9) was most suitable for cancer detection. For Mucin, our data indicates that construct A1 composed of T7 promoter, and T7 terminator was the most efficient with a concentration of 1.83(µg/mL). For Her-2 the most effective construct was B3 with a concentration of 0.90(µg/mL), which contains the optimized TA prompter and T7 terminator. For Mammaglobin-B, the most effective construct was C6 which contains optimized TA promoter and T1 terminator, with a concentration 0.40(µg/mL). Please see our parts page for a detailed summary of each of our constructs and the compatibility of the different promoters and terminators we utilized.
We performed two trials for each construct to increase the accuracy of the measurement. The values in the table are the averaged values of our two trials. One thing to note about our measurements is that whenever we discovered an error in our procedure, we performed further experiments to confirm our findings. For example, we noticed that we had a negative reading for construct A1 during our first trial, and repeated the experiment again. We hypothesized that this error was caused by inaccurate pipetting as members were unfamiliar with the 96-well plate reader and construction of standard curve. Upon repeating the experiment again, we were able to get a data reading that was accurate and reliable.
Additionally, in the results table, the word “NA” indicates that the values were incalculable under the BSA model. We decided to omit any results that were impossible to extrapolate from the concentration vs absorbance standard curve because doing so would be statistical extrapolation of the BSA model. For future teams, we recommend that protein quantification experiments be repeated multiple times due to the sensitivity of the equipment and high precision skills required for these kinds of assays.
For the successful constructs from the experiments(BBa_K3813011, BBa_K3813013, and BBa_K3813025) we decided to test these in our aptamer-based experiments to compare and account for any differences between E.Coli synthesized biomarker and ordered biomarker from the lab.
Aptamer-Based Testing Experiments
The experiments we conducted only indicated that our construct was successfully expressed and purified, thus, we decided to take a step further by verifying the function of our synthesized biomarkers. This would serve to see if our designed biomarker was fully compatible and functional for future experiments.
After establishing that our aptamer and biomarker were able to bind each other, we compared the binding affinity of our synthesized and ordered biomarkers to our aptamers. We theorized that if the biomarker was not functional, not only would it not bind to the aptamer(detection probe), it would also not be able to respond to the antibody in an ELISA assay. Therefore, we hypothesized that if the absorbance reading in the ELISA assay for both the synthesized biomarker and ordered biomarkers are relatively similar to each other, this would indicate successful synthesis and expression of our biomarkers. However, if there is little or no absorbance reading, this means that the biomarker was not able to bind to the aptamer or was unable to detect the antibody, two factors that indicate our biomarker is not functional.
Unfortunately, due to the global shipping crisis in September, we were unable to receive our equipment for ELISA in time to test for the binding affinity. We plan on verifying this in the second phase of our experiments next season but have successfully proven the production of recombinant biomarkers in an E.Coli season this year.
Please see our notebook page for a detailed protocol of the experiments we did and the parts page for detailed results on the production of each biomarker.
Aptamer-Based AuNP Colorimetric Assay
Our goal for iGEM was to design a portable testing kit for breast cancer that would function in similarity to a pregnancy test strip.
When researching portable designs, we came across HybriDetect developed by Milenia biotech, an universal lateral flow assay kit that utilized gold particles. Therefore, we decided to use gold nanoparticles as a way of showing how our design would work in the real world, serving as a proof-of-concept and framework for our project next year.
Gold nanoparticles have been versatile in the field of detection. This is commonly seen when excess salt is added to the gold solution, causing the nanoparticle to aggregate. This results in a solution change from red to blue, allowing for colorimetric detection.
We decided to utilize this concept by coating our gold nanoparticles with our aptamer. When our biomarker was added to the aptamer-coated AuNP, the aptamer coupled to the AuNP binds and undergoes a structural change, which causes aggregation and a color shift. When there is an absence of biomarker, the aptamer-coated AuNPs are dispersed and no color change occurs (Bosak et al., 2019).
Therefore, the utilization of AuNP allows for us to have a colorimetric based biosensor that allows for detection of biomarkers in breast cancer.
We constructed our gold nanoparticle detection system by utilizing the aptamers that had the strongest binding affinity to our biomarkers. By doing this, we were able to optimize our detection system to have the best detection capabilities.
In our experiments, we used different concentrations of biomarkers to help extrapolate a formula for the amount of biomarkers based on readings from absorbance and visual observation. We plan on using this information next year to develop a software that can read an image taken from a smartphone and convert this into an amount of biomarker in the body to allow for a convenient testing method for breast cancer.
Unfortunately, we were unable to order the materials for the AuNP experiments in time for the iGEM jamboree and test our system. We hope that our 2022 iGEM teams can continue our experiments.
However, we plan to test our system in the following manner, and anticipate the following results.
Incubate the aptamers with gold nanoparticles for 3-4 hours at room temperature. Make sure to protect from light with aluminum foil.
The protocol will need to be optimized for maximum detection so use different concentrations of aptamer to find the optimal one. Using too much aptamer will reduce the sensitivity of the assay
Add an equal volume of 20 mM HEPES, 2 mM MgCl2, pH 7.4 buffer and place the sample at 4 °C in the dark overnight. The aptamer-AuNP assay was in a 10 mM HEPES, 1 mM MgCl2, pH 7.4 solution.
Determine the initial salt concentration needed to cause a color change by using salt titration and determining the equivalence point. Add 20 µl of methanol (blank) to 180 µl of aptamer-AuNP assay in a 96-well plate.
Once optimization has been discovered through the titration, add 20 µl of analyte molecules(biomarker) diluted in methanol to 180 µl of aptamer-AuNP assay in a 96-well plate at room temperature. Immediately add the NaCl concentration determined in the previous step to initiate the assay color response
Obtain the largest color change possible by increasing or decreasing the concentration of NaCl, and compare the target response to the blank response.
Analyze the absorbance at 530 and 630 nm 150 seconds following NaCl addition and obtain a digital photo of the color change.
Plot and analyze the results as the ratio of absorbance obtained at 650 nm and 530 nm (E650/E530) as a function of analyte concentration.
We anticipate the following problems that will occur with our assay, and have developed the following methods to troubleshoot this.
The aptamers do not conjugate to the magnetic beads.
This could be due to a few reasons.
First, if too high or too little aptamer is used, this could mean that the sensitivity of the aptamer-AuNP system is impaired. Therefore, this issue can be troubleshooted by varying and optimizing the concentration of the aptamer used.
Next, this could also do with incubation time. If the incubation time is too short, the aptamer will be unable to conjugate to the magnetic bead, which means that we do not have a probe at all.
Therefore, investigating and modifying the incubation time is essential.
Finally, making sure that the aptamer-AuNP solution is not exposed to light is very important as this will keep the structures of the AuNP intact as any exposure to light can cause aggregation and false readings.
The biomarkers are not detected by the AuNP-assay
In order to troubleshoot this problem, one concentration of biomarker should not be used, but a standard curve of concentration vs absorbance should be constructed in order to measure for the detection capabilities of our assay and see the specificity of how well our system is able to detect biomarkers for breast cancer.