iGEM 2023 — Team mcmasteru

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{% block title %}Notebook{% endblock %}
{% block lead %}Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.{% endblock %}
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# Notebook

[Lab Notes](#labnotes)

## Lab Notes

All lab notes are organized in chronological order, and include a detailed description of experiments and wet lab work that happened on each day. They are included as PDFs for ease of viewing.

**May 2023 Notes:** [May 2023 Notes PDF](https://static.igem.wiki/teams/4819/wiki/lab-notes/may-compressed-notes.pdf)

**June 2023 Notes:** [June 2023 Notes PDF](https://static.igem.wiki/teams/4819/wiki/lab-notes/compressed-june-notes.pdf)

**July 2023 Notes:** [July 2023 Notes PDF](https://static.igem.wiki/teams/4819/wiki/lab-notes/july-notes.pdf)

**August 2023 Notes:** [August 2023 Notes PDF](https://static.igem.wiki/teams/4819/wiki/lab-notes/compressed-august-notes.pdf)

**September 2023 Notes:** [September 2023 Notes PDF](https://static.igem.wiki/teams/4819/wiki/lab-notes/compressed-september-notes.pdf)

**October 2023 Notes:** [October 2023 Notes PDF](https://static.igem.wiki/teams/4819/wiki/lab-notes/october-notes.pdf)

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{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a part of your project by going through at least one iteration of the engineering design cycle.{% endblock %}
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# Engineering

[Introduction](#intro)
[Optimization of fdGPS2.1 (RGD-4C)-Amp plasmid](#optimization-plasmid)
[Optimization of Modeling](#optimization-modeling)
[Optimization of Experimental Protocols](#optimization-experiment)
[References](#references)

## Introduction

The McMasterU team believes that adjustments are a fundamental part of research, providing the opportunity to learn from mistakes and refine ideas in order to generate novel products that benefit both the scientific community and the world at large. We put this into practice throughout the course of our project, where we designed a filamentous phage plasmid with the intention of producing phage particles to use in a vaccine to combat neglected parasitic diseases. Despite experiencing several obstacles while refining protocols and experiments in the process of constructing our plasmid, we were able to revise our errors and learn from them, granting us an even deeper understanding of our project. Through this learning curve, issues were troubleshooted and ultimately, breakthroughs were made with novel designs that helped our project reach the point it is at today.

## Optimization of fdGPS2.1 (RGD-4C)-Amp plasmid

The centerpiece of our project was the design and construction of the fdGPS2.1 (RGD-4C)-Amp plasmid that would generate phage particles. This design had several steps and was not an easy task to accomplish, as designing it required several hours of commitment and hard work to generate a product that would potentially work in application. As such, our dry lab team worked extensively on the development of the fdGPS2.1 (RGD-4C)-Amp plasmid through a sequence of iterations (see Figures 1-6), crafting each segment of the plasmid with care to avoid potential errors that may arise during its construction.

![](https://static.igem.wiki/teams/4819/wiki/eng-1.png)

Figure 1: Design schematic of fdGPS0.0.

![](https://static.igem.wiki/teams/4819/wiki/eng-2.png)

Figure 2: Design schematic of fdGPS0.0 with ampicillin resistance genes.

![](https://static.igem.wiki/teams/4819/wiki/eng-3.png)

Figure 3: Design schematic of fdGPS0.1(J23100) with ampicillin resistance genes.

![](https://static.igem.wiki/teams/4819/wiki/eng-4.png)

Figure 4: Design schematic of fdGPS0.2(J23100, RGD-4C) with ampicillin resistance genes.

![](https://static.igem.wiki/teams/4819/wiki/eng-5.png)

Figure 5: Design schematic of fdGPS1.2(J23100, RGD-4C) with ampicillin resistance genes.

![](https://static.igem.wiki/teams/4819/wiki/eng-6.png)

Figure 6: Design schematic of fdGPS1.2(J23100, RGD-4C) with kanamycin resistance genes.

Naturally, errors arose during the design cycle, yielding a product that was not viable during experimentation. This was the case with the fdGPS1.2(J23100, RGD-4C)-Kan plasmid iteration, which was initially designed to ligate together using a Gibson assembly method. However, we found the use of six sequences made it challenging for the experiment to be successful, as it was observed that the plasmid failed to construct during the ligation component of the experiment. We changed our approach and modified the restriction enzyme used for cutting the sequences, as well as the cutting sites on each sequence, to allow us to run a Golden Gate assembly which can accommodate a greater number of sequences. The new design for our plasmid (fdGPS2.1 - Amp) had a total of six sequences, two of which were PCR generated, that would be ligated together during Golden Gate assembly.

![](https://static.igem.wiki/teams/4819/wiki/eng-7.png)

Figure 7: Design schematic of fdGPS2.0 with ampicillin resistance genes. This schematic was generated with Golden Gate assembly overhang sequences instead of Gibson overhang sequences

![](https://static.igem.wiki/teams/4819/wiki/eng-8.png)

Figure 8: Design schematic of fdGPS2.1 (RGD-4C) with ampicillin resistance genes (designed using Benchling). Plasmid designs developed by William Pihlainen-Bleecker

## Optimization of Modeling

Modeling a biological response is a very big part of genetic engineering, especially for something with as far reaching effects as a vaccine that goes into our bodies. While we are a ways away from fully creating an efficient, safe, and effective vaccine, we have taken many steps to validate the design of our project. A more in depth explanation of our work and research is documented on other pages of our wiki, but we will provide a simplified overview here.

### Immunological Response Model

Firstly, to select the right antigens to stimulate an immune response, we referred to literature to recommend what might be good options to combat Echinococcosis and Cysticercosis. We selected TSOL18 and EG95 as our antigens of choice, despite these antigens having not been widely used in vaccination in earlier studies. Since our AAVP relies on the selection of immunogenic epitope sequences from these antigens to display on the viral coat, a predictive simulation with The Immune Epitope Database ([IEDB](http://tools.iedb.org/main/)) was needed to identify the best sequences from both proteins. Simulations are an incredibly important part of engineering success, as they allow us to inform the construction of our project without having to actually test it in organisms, which can be very harmful and financially costly if unsuccessful.

![](https://static.igem.wiki/teams/4819/wiki/eng-9.png)

Figure 9: Graph depicting the location of predicted immunogenic B cell epitopes (yellow regions) of the TSOL18 protein, obtained from an IEDB simulation (http://tools.iedb.org/bcell/).

We took advantage of additional tools by simulating an immune response to the antigens using online software. The results showed an inflammatory response, confirming immunogenicity of the selected antigens. However, this simulation performed better given the whole protein sequences instead of just the sequences of selected epitopes. We also used many default parameters for this simulation, as well as for the epitope prediction done earlier. Simulations are useful, but there is still much work to be done to get an accurate prediction such as specifying the amount of antigen to inject. We believe the most important thing about modeling is identifying these limitations and striving to create better models by building upon the work of others. By continuing our work on the AAVP and proceeding with further experiments in future engineering cycles, we will be able to validate the predictions of the models we use or suggest improvements.

![](https://static.igem.wiki/teams/4819/wiki/eng-10.png)

Figure 10: A prediction of the expected antibody response upon injection of the TSOL18 and EG95 proteins. The black line depicts the level of antigen (the injected proteins) while the other lines indicate different types of antibodies. This graphic was obtained from a C-ImmSim simulation (Rapin et al. 2010).

Our approach to modeling the effect of our AAVP extended well beyond using software tools as the team conducted extensive research into the expected and desired immune response to the AAVP, the mode of entry of the AAVP into the body and specific cell types, as well as the optimal delivery method and materials required to get the AAVP to its target location. We consider this research part of our approach to modeling and to engineering success as an understanding of all these diverse aspects of our project is essential to informing the construction of our AAVP and predicting its efficacy. Our work on the AAVP is a reflection of the intertwined complexities existing in any field of biology.

### Vaccine Distribution Model

It is important to be aware that projects in synthetic biology extend beyond just biology itself, as seen through our investigation of the optimal distribution strategy for our AAVP vaccine. For a vaccination campaign to be effective, it is essential to consider many parameters such as the quantity of vaccine available, who to distribute it to, and how quickly it can be distributed to name a few. This, in turn, is affected by determining who the most vulnerable demographics are, who is most likely to transmit the disease, how long the disease lasts, how long it takes for symptoms to appear, how severe the symptoms are, rates of transmission, and so on.

We come to see that vaccination depends on so much more than biological mechanisms – with socioeconomic and geopolitical factors immediately coming into play. Based on this, we searched for potential models to determine optimal vaccine allocation strategies which were often found to rely on data about vaccine efficacy, costs, and demographic information in order to produce meaningful output. Unfortunately, we are unable to reliably predict or determine this data at our project’s current stage, and thus attempted to recreate a simplified version of one such model (Wen et al., 2023). By deconstructing and rebuilding this model, we gained an understanding of the complexities of effective vaccination that extend beyond modeling synthetic biology. It provided us with an understanding of vaccine roll out based on the allocation of resources within a particular region, providing estimates of how many individuals would be given a dose within each priority group given the constraints of budget, storage capacity, transportation, and more as outlined in Figure 11. Throughout the course of this iGEM cycle, we gained an appreciation of the necessity of models for informing the optimal use of scientific innovations, particularly in areas that have been otherwise neglected. Moving forward, the collection of data such as the disease’s basic reproduction number, R0, in regions with an overlap of both diseases, such as Latin America, would add greater specificity to this optimization model for future global use.

![](https://static.igem.wiki/teams/4819/wiki/eng-11.png)

Constraints used in the function to maximize output of vaccine distribution in a specific area, i, after identifying parameters such as priority groups, target doses, costs and budget, storage capacity, and the percentages of vaccinated and unvaccinated individuals in a population. Constraint (2) ensures that the number of vaccinated individuals should not exceed the number of target population in the area. Constraint (3) ensures that the vaccination ratio for each priority is not lower than the minimum pre-set coverage rate to prevent low vaccination rates. Constraint (4) indicates priority be given to those who have received less than the doses administered. Constraint (5) represents the relationship between the amount of vaccine allocated and the number of people vaccinated, considering vaccination history. Constraint (6) is the constraint that the total allocation of vaccines in all demand points does not exceed the stock in the allocation center and (7) is that the amount of vaccines obtained in each region does not exceed its warehouse capacity. Constraint (8) indicates that the total cost of vaccine distribution and vaccination in each region does not exceed the local budget.

## Optimization of Experimental Protocols

### Optimization of PCR protocol

Before carrying out our Golden Gate assembly, we first needed to optimize our PCR protocol to make sure that we had a sufficient concentration of PCR product for a successful plasmid construction. An analysis of our initial experiments revealed that the PCR yield of one of our plasmid fragments was lower than we needed. Through several PCR tests and gel electrophoresis analyses of the results we were able to optimize the protocol for the PCR of each individual fragment, particularly by systematically testing different temperatures and durations for the various steps of the PCR cycle. We discovered that the optimal temperature for functioning of each single stranded DNA primer ordered from [IDT](https://www.idtdna.com/pages) was approximately 57.5 degrees Celsius for the forward and reverse primers elongating our shorter fragment, and 70 degrees Celsius for the forward and reverse primers elongating our longer fragment. This was confirmed after running both primers at their respective temperatures and then observing the DNA samples on an agarose gel.

### Optimization of Gel Electrophoresis protocol

After optimizing our PCR protocol, we turned our attention to improving how we ran our gel electrophoresis protocol, which generated smears while running (Figure 13). Upon further investigation, we found that the use of higher voltages was one of the issues that caused our bands to bend and smear as they traveled down the gel. To fix this, we reduced the voltage from 110 V to between 70-90 V, and increased the time the gel ran for to allow the bands to migrate far enough down the gel. Another resolution to the smearing was decreasing the volume of the loaded sample into each well. We discovered that we were adding too much sample to each well, which caused overflow and loss of some of our sample. Once we reduced our volumes to approximately 24 uL (8-well comb, 35 mL of agarose gel), the bands showed less smearing and distortions (Figure 14).

![](https://static.igem.wiki/teams/4819/wiki/eng-12.png)

Figure 12: Gel electrophoresis using DNA samples. Smearing and distortions are present in the DNA ladder and samples.

![](https://static.igem.wiki/teams/4819/wiki/eng-13.png)

Figure 13: Gel electrophoresis using DNA samples. Smearing and distortions have reduced significantly compared to the previous figure.

### Optimization of Gel Purification protocol

Extraction of the DNA samples from the agarose gel presented a new problem for our team. Using a razor blade, we carefully removed a gel slice that contained our sample DNA, and proceeded to dissolve it using a Monarch Gel Extraction Kit ([T1020S](https://www.neb.com/en/products/t1020-monarch-dna-gel-extraction-kit#Product%20Information)). After performing a nanoquant analysis, we realized the data we were obtaining was not viable for use in a Golden Gate assembly experiment. Despite trying several times with this purification method, our data did not yield results we could use for our plasmid construction. Therefore, we pivoted to using a Monarch DNA/PCR Cleanup Kit ([T1030S](https://www.neb.ca/T1030)) to purify our sample without having to extract it from agarose gel. Once we made this adjustment, we found that the data we observed (Table 1) were viable for a Golden Gate assembly, allowing us to push forward with our project.

![](https://static.igem.wiki/teams/4819/wiki/eng-14.png)

Table 1: Nanoquant analysis results after DNA/PCR Cleanup of f3-55nm DNA fragments.

### Optimization of Bacterial Transformation protocol

While practicing techniques for experiments, as well as performing experiments to generate data, we found that our bacterial transformation protocol needed to be modified. While performing the experiment, we found several instances where we were performing protocols either incorrectly or not as efficiently as we could have been. Such moments include heat shocking the bacteria cells for the appropriate amount of time, the time given for incubation after shock, and the concentration of antibiotics used for LB agar plates. Our original approach to performing transformations ultimately yielded results that were not viable, leading us to optimize the protocol to avoid future issues. These changes included heat shocking at 42 degrees Celsius for 30 seconds, incubating the bacteria cells for up to an hour, testing the concentration of ampicillin and tetracycline added to the LB agar plates and comparing them, adding a serial dilution step to test the optimal dilution factor, and implementing streaking to separate colonies (Figure 14). These changes proved to be useful when running future transformation experiments, allowing us to generate colonies that would be used for future research.

![](https://static.igem.wiki/teams/4819/wiki/eng-15.png)

Figure 14: Streaked plates of NEB Stable Competent E. coli cells containing f3-55nm and fMCS plasmids.

## References

IEDB. Antibody epitope prediction [Internet]. 2023 [cited 2023 Sep 10]. Available from: http://tools.iedb.org/bcell/

Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PloS one. 2010 Apr 16;5(4):e9862.

Wen Z, Yue T, Chen W, Jiang G, & Hu, B. Optimizing COVID-19 vaccine allocation considering the target population. Front Public Health. 2023 Jan 6;10:1015133.

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# Experiments

[Introduction](#intro)
[Construction of the plasmid](#construction)
[Ancillary Experiments](#ancillary)
[Protocols used during Experimentation](#protocols)
[References](#references)

## Introduction

The research we conducted involved assembling an adeno-associated virus phage (AAVP) plasmid that would generate phage particles. The following experiments were conducted in our efforts to construct our fdGPS2.1(RGD4C)-Amp plasmid, which include experiments that amplify DNA sequences, propagate viral progeny, and analyze our data.

## Construction of the plasmid

**PCR -** To generate DNA sequence needed for our plasmid construction, we amplified two different segments within the f3-55nm plasmid. Taq DNA polymerase (link), nucleotide bases (link), and specially crafted primers generated at IDT were used to amplify our samples as they underwent several heat cycles. The heat cycles generated millions of copies of both fragments we targeted, allowing us to analyze and then isolate the copies.

![](https://static.igem.wiki/teams/4819/wiki/exp-1.png)

Figure 1: Fact sheet about Polymerase Chain Reaction (PCR) (image acquired from [National Human Genome Research Institute](https://www.genome.gov/about-genomics/fact-sheets/Polymerase-Chain-Reaction-Fact-Sheet))

**Gel Electrophoresis -** After generating our PCR products, we used gel electrophoresis to separate the DNA fragments over the length of an agarose gel, allowing us to assess the efficiency of amplification within the DNA segments. The PCR samples were mixed with purple loading dye and then loaded into a solidified agarose gel before passing a current through it. Once completed, the gel is soaked in a solution of 1x SYBR green DNA staining solution ([S7563](https://www.thermofisher.com/order/catalog/product/S7563?SID=srch-srp-S7563)), and then visualized using either a UV transilluminator or Chemidoc imaging tool. Given that both fragments were different sizes, we could easily visualize them with respect to each other and to the 1 kb DNA ladder ([N3232S](https://www.neb.ca/N3232)).

**Golden Gate Assembly -** Once we generated our PCR samples and ordered our specially crafted sequences from IDT, we performed a Golden Gate assembly to ligate all sequences into one plasmid. was used to ligate several DNA sequences together to generate a plasmid. By using the Type IIS restriction enzymes BbsI-HF ([R3539S](https://www.neb.com/en/products/r3539-bbsi-hf#Product%20Information)), we generated cuts in several DNA sequences at the same time within one reaction tube. After this occurs, T4 DNA ligase ([Mo202L](https://www.neb.com/en/products/m0202-t4-dna-ligase#Product%20Information)) re-combines these sequences together during an incubation step to assemble the plasmid product.

**Bacterial Transformation -** As an initial test of the plasmid’s function, we transformed it into NEB 10-Beta competent cells ([C3019I](https://www.neb.ca/C3019)). This involved adding the bacterial cells and the newly constructed plasmid to the same tube and then heat shocking them in a water bath. After this step, the bacteria were left to incubate overnight on LB agar plates containing ampicillin to allow only cells with a viable plasmid to grow. To test the efficiency of transformation and growth of cells, we used varying concentrations of ampicillin to determine the lowest level of antibiotic required to isolate cells with the plasmid.

![](https://static.igem.wiki/teams/4819/wiki/exp-2.png)

Figure 2: Workflow of a Bacterial Transformation (image acquired from [Khan Academy](https://www.khanacademy.org/science/biology/biotech-dna-technology/dna-cloning-tutorial/a/bacterial-transformation-selection))

**Miniprep -** After propagating our plasmid, we planned to isolate it using a Monarch miniprep kit ([T1010S](https://www.neb.ca/T1010)). This would involve spinning down an inoculum containing bacterial cells with the plasmid, forming a pellet. Isolating the plasmid from this pellet would involve several wash steps, ultimately generating a solution containing the plasmid suspended in DNA elution buffer ([T1016L](https://www.neb.ca/T1016)). This solution would then undergo analysis using a nanoquant tool ([plate reader](https://biointerfaces.mcmaster.ca/services/our-equipment-instruments/plate-reader-infinite-m200-pro-tecan/)) to determine its purity (260/280 ratio) and its concentration.

**Restriction Digest -** Following the miniprep, we would run a restriction digest using known restriction sites found in our plasmid. If our plasmid formed successfully, the digest would generate two fragments that could be visualized during gel electrophoresis. To do this, we would run the sample using SacII ([R0157S](https://www.neb.ca/R0157)) and KpnI ([R3142S](https://www.neb.ca/R3142)) restriction enzymes to generate fragments 2685 base pairs and 4387 base pairs long. This would be accomplished by incubating the samples in a water bath to activate the restriction enzymes, followed by incubation at a higher temperature to deactivate them to prevent further digestion of DNA sequences.

![](https://static.igem.wiki/teams/4819/wiki/exp-3.png)

## Ancillary Experiments

**Inoculation -** To ensure we isolated the plasmid of interest from our transformation experiment, we extracted a single colony from a petri dish containing the transformed bacteria and added it to an LB broth solution containing antibiotics. The sample was then incubated overnight in a shaking incubator to generate a higher density of bacteria containing the plasmid.

**DNA/PCR Cleanup -** After observing the presence of our PCR products on agarose gel, we purified the samples using a Monarch DNA/PCR Cleanup Kit ([T1030S](https://www.neb.ca/T1030)). This involved filtering the DNA samples using several wash steps, removing any impurities such as excess nucleotides, primers, and DNA polymerase. The sample is then analyzed using the nanoquant tool to determine its purity and concentration beforing using it in future experiments.

**Gel Extraction -** During the initial stages of our experiments used to isolate DNA fragments that were separated on agarose gel. Using either a razor blade or scalpel to cut the gel slice out, the sample was then dissolved using a Monarch Gel Extraction Kit ([T1020S](https://www.neb.com/en/products/t1020-monarch-dna-gel-extraction-kit#Product%20Information)) purchased from NEB and then washed several times to produce a purified DNA sample.

## Protocols used during Experimentation

[PCR](https://static.igem.wiki/teams/4819/wiki/2023-lab-protocols/pcr-isolation-of-fragments-from-f3-55-nm-prt-25wmkgik.pdf)

[Gel Electrophoresis](https://static.igem.wiki/teams/4819/wiki/2023-lab-protocols/gel-electrophoresis-prt-wqtk0p8o.pdf)

[Golden Gate assembly](https://static.igem.wiki/teams/4819/wiki/2023-lab-protocols/golden-gate-assembly-for-f3-55-nm-pcr-fragments-using-bbsi-hf-restriction-enzyme-prt-l2gh67zy.pdf)

[Bacterial Transformation](https://static.igem.wiki/teams/4819/wiki/2023-lab-protocols/transformation-and-inoculation-of-fd-tet-derived-phages-prt-km9h6txh.pdf)

[Miniprep](https://static.igem.wiki/teams/4819/wiki/2023-lab-protocols/miniprep-prt-9cd6rnk1.pdf)

[Restriction Digest](https://static.igem.wiki/teams/4819/wiki/2023-lab-protocols/restriction-digest-prt-hnjqdrrc.pdf)

## References

Addgene: Molecular Biology Protocol - Restriction Digest of Plasmid DNA [Internet]. [cited 2023 Oct 11]. Available from: https://www.addgene.org/protocols/restriction-digest/

Bacterial transformation & selection (article) | Khan Academy [Internet]. [cited 2023 Oct 11]. Available from: https://www.khanacademy.org/\_render

Genome.gov [Internet]. [cited 2023 Oct 11]. Polymerase Chain Reaction (PCR) Fact Sheet. Available from: https://www.genome.gov/about-genomics/fact-sheets/Polymerase-Chain-Reaction-Fact-Sheet

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{% block title %}Results{% endblock %}
{% block lead %}You can describe the results of your project and your future plans here.{% endblock %}
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# Results

[Generation of PCR products for use in Golden Gate assembly](#golden-gate)
[Construction of the fdGPS2.1(RGD4C)-Amp plasmid](#plasmid)
[Next steps regarding generation and analysis of phage particles](#next-steps)
[References](#resultsreferences)

## Generation of PCR products for use in Golden Gate assembly

The f3-55nm plasmid created by Dr. George P. Smith was used as a template to generate both PCR products that would be used during Golden Gate assembly. Both primers necessary for this experiment were created by IDT and targeted different locations on the main plasmid. Primer 1 generated a fragment 1,476 base pairs long, while Primer 2 generated a fragment 2,997 base pairs long.

Both products were visualized using a 1.5 % agarose gel to separate the PCR sample from the primers and other impurities (Figure 1). Although smearing occurred during several attempts of this experiment, we found that both PCR samples were observed on the gel and migrated to the appropriate positions with respect to the DNA ladder. It became clear that our samples contained the amplified fragments from the f3-55nm plasmid and were ready for purification

![](https://static.igem.wiki/teams/4819/wiki/header-images/result-1.png)

Figure 1: Gel electrophoresis of negative control (second well), Primer 1 PCR product (fourth well), and Primer 2 PCR product (sixth well).

### Purification of PCR products

Our initial attempts to purify the samples was done using a gel extraction kit to excise the DNA from the gel and then purify it through several wash cycles. Unfortunately, this did not yield favorable results (Table 1) for a Golden Gate assembly and required us to troubleshoot our purification method. We eventually changed our purification protocol to a DNA/PCR cleanup using a DNA/PCR cleanup kit ([T1030](https://www.neb.ca/T1030)), which demonstrated significantly better results (Table 2). Given the favorable concentration and ratio of both PCR products, we decided to use them in the Golden Gate assembly experiment to construct our plasmid.

![](https://static.igem.wiki/teams/4819/wiki/header-images/result-2.png)

Table 1: Nanoquant results of DNA fragment product generated from second set of primers (product from first set of primers was not observed in the gel).

![](https://static.igem.wiki/teams/4819/wiki/header-images/result-3.png)

Table 2: Nanoquant results of DNA fragment products generated from first and second set of primers.

## Construction of the fdGPS2.1(RGD4C)-Amp plasmid

### Ligation of DNA fragments using Golden Gate assembly

Construction of the fdGPS2.1(RGD4C)-Amp plasmid involved the combination of both PCR products generated in previous experiments as well as four other sequences that were created by IDT. The volumes of each sequence was determined using [Coleman’s Lab protocol](http://coleman-lab.org/wp-content/uploads/2021/05/Golden-Gate-Assembly.pdf) to make an equimolar solution (Table 3). The restriction enzyme BbsI-HF ([R3539S](https://www.neb.com/en/products/r3539-bbsi-hf#Product%20Information)) was introduced into the solution to generate cuts in each sequence, as well as T4 DNA ligase ([Mo202L](https://www.neb.com/en/products/m0202-t4-dna-ligase#Product%20Information)) to recombine the newly modified sequences.

![](https://static.igem.wiki/teams/4819/wiki/header-images/result-4.png)

Table 3: Data involving sequences used for Golden Gate assembly (protocol referenced from Coleman’s Lab). PCR 1 fragment is bolded due to it having the highest ng/kb value and is a reference for other volume calculations.

### Bacterial Transformation of fdGPS2.1(RGD4C)-Amp plasmid product

Once we completed the Golden Gate assembly, we transformed the fdGPS2.1(RGD4C)-Amp plasmid using NEB 10-Beta competent E. coli cells to test the viability and function of it. What we found was the plasmid did not convey ampicillin resistance to the bacterial cells as they died when incubated overnight on LB agar plates containing ampicillin (Figure 2). We believe this could have been due to several factors including the Golden Gate assembly experiment not being successful, potential errors made during the bacterial transformation experiment, and/or potential issues with the design of the fdGPS2.1(RGD4C)-Amp plasmid.

![](https://static.igem.wiki/teams/4819/wiki/header-images/result-5.png)

Figure 2: Bacterial transformation of fdGPS2.1(RGD4C)-Amp plasmid using NEB 10-Beta competent E. coli cells. The experimental plates at the top of the image (β-1 on the left and β-2 on the right) contained our plasmid and did not demonstrate any growth. The control plates at the bottom of the image (β + on the left and β - on the right) did not contain our plasmid, demonstrating growth for the positive control which contained pUC19 while demonstrating no growth for the negative control which contained nuclease-free water.

## Next steps regarding generation and analysis of phage particles

### Optimization of protocols

Although we did not succeed in producing a functioning plasmid that would generate phage particles, we have plans for progressing towards that goal. Firstly, our dry lab team will investigate potential issues that may have been present when designing the fdGPS2.1(RGD4C)-Amp plasmid, and make modifications to how the fragments combine during Golden Gate assembly (Figure 3). This will be done in parallel with the wet lab team, which will investigate potential issues associated with how the experiments are run and optimize them to improve our chances of the plasmid assembling.

![](https://static.igem.wiki/teams/4819/wiki/header-images/result-6.png)

Figure 3: Schematic design of fdGPS2.1(RGD4C)-Amp plasmid (designed using Benchling)

### Purification & Analysis of fdGPS2.1(RGD4C)-Amp phage particles

Once we have successfully assembled our plasmid and transformed it, our next step would be to isolate the phage particles that are generated from the bacteria cells. We would achieve this using a PEG/NaCl isolation protocol to purify the phage sample. After purification, we would focus on quantifying the titer of phage particles present by using a [double layer agar overlay assay](https://link.springer.com/chapter/10.1007/978-1-0716-1932-2_14). After determining the viral titer, we would use Electron Microscopy to obtain a real time image of our phage particle’s morphology and size. This experiment would also be accompanied by an SDS-PAGE to analyze the binding protein, RGD-4C, and antigens, TSOL18 and EG95, attached to the phage’s coat proteins. This would allow us to observe whether the peptides are present on the phage surface and if the phage itself is ready for functional testing.

To test for the function of the fdGPS2.1(RGD4C)-Amp phage, we would utilize a dendritic cell line and observe whether the cell binds to and engulfs the phage particles upon interaction. To determine if the phage was phagocytosed, we would add a fluorescent label to it and track the presence of fluorescence within the dendritic cell after the interaction has occurred.

## References

da Silva RT, de Souza Grilo MM, Magnani M, de Souza Pedrosa GT. Double-Layer Plaque Assay Technique for Enumeration of Virus Surrogates. In: Magnani M, editor. Detection and Enumeration of Bacteria, Yeast, Viruses, and Protozoan in Foods and Freshwater [Internet]. New York, NY: Springer US; 2021 [cited 2023 Oct 11]. p. 157–62. (Methods and Protocols in Food Science). Available from: https://doi.org/10.1007/978-1-0716-1932-2\_14

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