iGEM 2022 — Team hus-united

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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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## What should this page contain?

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* Chronological notes of what your team is doing.
* Brief descriptions of daily important events.
* Pictures of your progress.
* Mention who participated in what task.

## Inspirations

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* [2018 Munich](http://2018.igem.org/Team%3AMunich/Notebook)
* [2019 Georgia State](https://2019.igem.org/Team%3AGeorgia_State/Notebook)
* [2019 Newcastle](https://2019.igem.org/Team%3ANewcastle/Notebook)
* [2020 IISER Pune India](https://2020.igem.org/Team%3AIISER-Pune-India/Notebook)
* [2020 Lund](https://2020.igem.org/Team%3ALund/Notebook)
* [2020 NOVA LxPortugal](https://2020.igem.org/Team%3ANOVA_LxPortugal/Notebook)
* [2020 RDFZ China](https://2020.igem.org/Team%3ARDFZ-China/NoteBook)

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Engineering Success

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The main goal of our project is to use degradation-tuning RNAs to enhance protein concentrations without significantly affecting bacterial growth. First, we have to validate whether our assumption of changing mRNA degradation rate to control gene expression is correct, using in-silico approaches, say kinetic models. Then we choose several published sequences and test them in E.coli DH5α strains for experimental implementation. After detailed data analysis and learning, we re-designed a well-performed dtRNA using forward-engineering approaches, predicted its secondary structures using computational tools, and test it in our lab.

# Design

At the design stage, we established a kinetic model of gene expression and bacteria growth to test the viability of our basic idea. Since we want to develop a dtRNA-based method for enhancing protein concentrations that surpasses conventional means such as adopting stronger promoters, the major goal of our model should somehow verify the effect of changing the mRNA degradation rate constant to bacterial growth and protein concentration, and compare it with changing the promoter strength. Therefore, we established a kinetic model that integrates gene expression-degradation and negatively feedbacked cell growth. We search the parameters of different parts from the parts registry and literature, substitute them into the model, and the results show that reducing the degradation rate has a negligible impact on cell growth compared to enhancing promoter strength if assuming the same gene expression levels.

![](http://parts.igem.org/wiki/images/b/b4/Engineering_1.png)

Figure.1 Scheme for gene expression and degradation

![](http://parts.igem.org/wiki/images/8/8f/Engineering_2.png)

Figure.2 effects of changing mRNA degradation rates on cell growth and protein concentration

To test the effectiveness of dtRNAs in DH5α strains, we choose six RNA structures in the literature that show the capability of manipulating RNA degradation in DH10B. Integrating dtRNA structures in GFP-expressing cassettes is well-suited for measurements. The fluorescence curve could indicate protein concentration, while OD600 indicates bacterial growth.

# Build

At the build and test stage, we use the HiFi assembly cloning method to integrate dtRNAs in GFP-expressing cassettes. We have encountered a lot of problems in this section, and the most serious one is the unsuccessful transformation. After carefully considering the experimental protocol, we found several factors that would strongly affect the assembly process:

1. We use overlapping primers that contain dtRNA sequences and homology arms for HiFi assembly, so an annealing process is critical for the formation of duplexes as integration segments. At first, we use 1xTAE buffer for annealing, in which EDTA could become an inhibitory component for polymerases when mixing it with the HiFi mix. To avoid this situation, we changed the annealing buffer to NEBuffer 3 which is optimized for enzyme digestion and compatible with HiFi assembly systems.

2. We changed the overlapping primers to their reverse-complementary sequences so that the 3’ end of integration fragments would bond to the vector, facilitating elongation and integration, therefore the yield of HiFi assembly.

3. The initial ice-making conditions are poor in our lab so we are unable to get well-crushed ice for transformation experiments, which may affect the activity of competent cells. So we purchased a new ice-making machine and solve this problem.

# Test

After making the aforementioned adjustments, we successfully transferred the plasmids into DH5α E.coli strains, then the transformants are sequencing-verified, and then collect the fluorescence and OD curve. Compared with the previous report from the literature, we observed comparable fluorescent signals, indicating the effect of degradation-tuning RNAs is indeed transferrable to DH5α strains, the only difference is that the introduction of dtRNA82 did not abolish the GFP fluorescence, which may come from the difference of Rnase targeting sites between the two strains.

![](http://parts.igem.org/wiki/images/a/a8/Engineering_3.png)

Figure.3 GFP fluorescent curve under the control of J23106

![](http://parts.igem.org/wiki/images/5/51/Engineering_4.png)

Figure.4 Comparasion of dtRNA fluorescent fold change with different host strains

On the other hand, the measured OD600 curve shows a similar but even faster growth curve when using dtRNAs, no matter whether its function is to resist or facilitate degradation. This interesting observation deserves further investigation in the future. These experiment results solidified dtRNAs’ ability to change expressions and confirm our simulation results of not posting burden to the host.

![](http://parts.igem.org/wiki/images/a/a1/Engineering_5.png)

Figure.5 Bacterial growth curve under the control of J23106

# Learn

After the aforementioned experiment results, we firmly demonstrate the effect of dtRNAs. Still, we wonder if there is a limit to resistance to the degradation of dtRNAs. Since the dtRNA structures published in the article are only generated randomly in the first generation, there is space for rationally designing new dtRNA structures. Therefore, we wish to design a forward-engineered dtRNA according to the relationship between structure and function to further expand its dynamic range in the next cycle.

# Next Cycle

In the next cycle, we firstly designed a second-generation dtRNA(dtRNA1v2) with optimal structural parameters based on the structure of dtRNA1 and used NUPACK to simulate the secondary structure.

![](http://parts.igem.org/wiki/images/8/82/Engineering_6.png)

Figure.6 Simulated secondary structure of dtRNA1v2

The structure parameters of dtRNA1v2:

1. stem length: 11 bp; structural factor: 100%

2. stem GC content: 54.5%; structural factor: 91%

3. loop size: 6 nt; structural factor:100%

Then we experimentally integrate dtRNA1v2 into GFP-expressing cassettes and measure the fluorescence and OD value, same as the first cycle.

![](http://parts.igem.org/wiki/images/c/c7/Engineering_7.png)

Figure.7 Fluorescence curve of dtRNA1v2

The result shows a slightly higher fluorescence value compare to the original version of dtRNA1, with the GFP fold change up to 3.96. Although this value may not be statistically significant when adding repeating groups, it shows the potential of designing second-generation dtRNAs using forward-engineering principles.

All in all, we think we have demonstrated our engineering success by using in silico simulation to guide our design and experiments and applying rational design principles to further optimize our basic parts.

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{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
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## What should this page contain?

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Describe the research, experiments, and protocols you used in your iGEM project. These should be detailed enough for another team to repeat your experiments.

If you made Parts this year, please remember to put all information, characterization, and measurement data on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).

## Inspirations

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* [2019 Nantes](https://2019.igem.org/Team%3ANantes/Experiments)
* [2019 TU Eindhoven](https://2019.igem.org/Team%3ATU_Eindhoven/Experiments)
* [2019 Mingdao](https://2019.igem.org/Team%3AMingdao/Demonstrate)
* [2020 Amsterdam](https://2020.igem.org/Team%3AAmsterdam/Experiments)
* [2020 NCTU Formosa](https://2020.igem.org/Team%3ANCTU_Formosa/Experiments)
* [2020 USAFA](https://2020.igem.org/Team%3AUSAFA/Experiments)

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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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## What should this page contain?

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* Clearly and objectively describe the results of your work.
* Future plans for the project.
* Considerations for replicating the experiments.

## Describe what your results mean

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* Interpretation of the results obtained during your project. Don't just show a plot/figure/graph/other, tell us what you think the data means. This is an important part of your project that the judges will look for.
* Show data, but remember **all measurement and characterization data must also be on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).** Otherwise these data will not be in consideration for any medals or part awards!
* Consider including an analysis summary section to discuss what your results mean. Judges like to read what you think your data means, beyond all the data you have acquired during your project.

## Project Achievements

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You can also include a list of bullet points (and links) of the successes and failures you have had over your summer. It is a quick reference page for the judges to see what you achieved during your summer.

* A list of linked bullet points of the successful results during your project
* A list of linked bullet points of the unsuccessful results during your project. This is about being scientifically honest. If you worked on an area for a long time with no success, tell us so we know where you put your effort.

## Inspirations

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* [2019 Newcastle](https://2019.igem.org/Team%3ANewcastle/Results)
* [2019 Munich](https://2019.igem.org/Team%3AMunich/Results)
* [2019 Tec Chihuahua](https://2019.igem.org/Team%3ATec-Chihuahua/Results)
* [2020 Aalto Helsinki](https://2020.igem.org/Team%3AAalto-Helsinki/Results)
* [2020 GreatBay SCIE](https://2020.igem.org/Team%3AGreatBay_SCIE/Results)
* [2020 Queens Canada](https://2020.igem.org/Team%3AQueens_Canada/Results)

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