Partnership

Information on Partner

Gothenburg Logo

Team name: Chalmers-Gothenburg

Gothenburg Members

Project title: MOD^3 (Modular DNA Detection Device)

Gothenburg Project

Project summary: Chalmers-Gothenburg are pursuing two new methods for DNA detection. The first method is cell-free and is based on the dCas9 molecule which will recognize a specific parasitic DNA sequence, and in turn cause a protease to activate the enzyme beta-galactosidase. This enzyme will cleave the substrate X-gal (a chromogenic substrate) and cause a blue visible read-out. Thus, a test tube containing all the required molecules in addition to the bodily fluid, will turn blue in the presence of parasitic DNA, and remain colourless otherwise. The second method is cell-based and will utilise a modified strain of regular baker’s yeast. The yeast will recognize parasitic DNA using zinc fingers fused to outer membrane proteins. On the inside of the cell, the membrane proteins will be fused to synthetic transcription factors (TFs) that will be cleaved off in the presence of parasitic DNA. The TFs will then promote the production of an enzyme that will yield the purple molecule violacein, allowing for a visible read-out.

Overview

We iGEM UTokyo and iGEM Chalmers-Gothenburg collaborated on many points throughout the project. First, during the planning phase of the project, we held meetings to share each other’s project details and receive feedback. During the building phase of the project, Chalmers-Gothenburg's modeling led to improvements in the design of UTokyo’s gene circuit, and UTokyo's assays led to improvements in both teams' designs. In addition, at the stage of spreading the project, we decided to cooperate in various ways, such as translating Chalmers-Gothenburg's blog into Japanese and creating the gene circuit of this year’s Chalmers-Gothenburg’s project in Genochemy, our education tool, to spread each of our project throughout society.

Why we collaborated

UTokyo wanted to create a yeast that decodes the password only when exposed to light in a specific order, but with our original design, the probability of recombination did not increase even when exposed to light for a sufficient amount of time, and the gene circuit sometimes did not progress as we expected. However, using the Worm-like chain model proposed by Chalmers-Gothenburg, it was found that the probability of recombination could be increased by shortening the sequence length between the two recognition sites, leading to an improved design. (See Modeling page)

Chalmers-Gothenburg used a DNA binding protein called zinc finger to detect the DNA for Schistosomiasis testing. Since UTokyo intended to use synTALE as a DNA binding protein, we compared synTALE and zinc finger in experiments to determine which was more suitable for both of our projects.

When & How we collaborated

Around June, we made our first contact on Slack. Both teams planned to use dCas9, so collaboration related to dCas9 was planned.

June 22

June 22 mtg

First meeting was held. UTokyo suggested using scRNA in the dCas9 system of Chalmers-Gothenburg's project. In addition, it was discovered that UTokyo was using synTALE and Chalmers-Gothenburg was using zinc finger as DNA binding proteins, so we began to consider the possibility of comparing the two.

Later, UTokyo changed the direction of the project and decided to use recombinase instead of dCas9, so we proceeded in the direction of synTALE and zinc finger.

July 19

The Worm-like chain model used by Chalmers-Gothenburg was shared on Slack, and it was decided to have a meeting to discuss the possibility of using it in UTokyo’s project as well. We had a meeting about the Worm-like chain model; Chalmers-Gothenburg gave us the MATLAB code and UTokyo discussed it in the team, and concluded that it could be used for modeling of PA-Cre.

August 24

Got a proposal to translate Chalmers-Gothenburg's blog into Japanese; UTokyo translated it and sent the draft to them on September 10.

September 4

We had a meeting on PA-Cre, zinc finger, and synTALE, and exchanged ideas on using the Worm-like chain model for PA-Cre. Later, UTokyo found that this model was more effective for the modelling of recombination than that of PA-Cre. UTokyo performed the simulation using this model and obtained significant results. It was decided that UTokyo will perform an assay for zinc finger and synTALE.

September 21

UTokyo proposed a collaboration using Genochemy, an online education tool developed by UTokyo.

October 6

UTokyo conducted the assay and found that synTALE binds to DNA more strongly than zinc finger. UTokyo reported the result to Chalmers-Gothenburg and suggested that they use synTALE instead of zinc finger to improve the system.

What we offered

zinc finger and synTALE

The Chalmers-Gothenburg team uses a DNA binding protein called zinc finger to detect DNA in Schistosomiasis testing. Since UTokyo planned to use a DNA binding protein called synTALE in a system that promotes gene expression when exposed to red light, we found that it would be beneficial for both teams to compare the properties of zinc finger and synTALE. Therefore, it was decided that UTokyo would conduct an experiment to compare the binding strength of zinc finger and synTALE to DNA. Specifically, we prepared zinc finger and synTALE with the target sequence near the TATA box of the promoter, and compared them by the degree to which the downstream mCherry fluorescence was weakened when each was expressed. The results showed that synTALE had a significantly stronger binding ability to DNA compared to zinc finger (Figure 1 and 2, please see Results page for details). Based on this, UTokyo suggested to Chalmers-Gothenburg to use synTALE instead of zinc finger, thus contributing to the improvement of the design.

Figure 1

Figure 1. Distribution of fluorescence. 313 cells of control, 210 of synTALE, and 170 of zinc finger were measured.

Figure 2

Figure 2. Average of fluorescence. Error bars indicate standard errors. Average of fluorescence. Error bars indicate standard errors. * denotes a significant difference at p<0.05p<0.05, ** denotes a significant difference at p<0.01p<0.01, and n.s. denotes no significant difference. The statistical test was conducted using the Wilcoxon rank sum test.

Scaffold RNA

Since we initially considered using dCas9 and scaffold RNA (scRNA), When we heard about Chalmers-Gothenburg's project design, we thought that their design would be better if they used scRNA. As a result, they began to consider using scRNA as an improvement for their project. For details, please see their Partnership page.

Genochemy (Planning)

UTokyo has developed an online game called Genochemy as an educational tool. It allows users to design genetic circuits and simulate their output in a browser. It is user-friendly, so even young people unfamiliar with synthetic biology can learn synthetic biology while playing with it. Therefore, we thought that if we could display the gene circuits of the iGEM project on Genochemy, it would be easier to explain the project and make it more comprehensible. Since UTokyo’s projects were already available on Genochemy, we newly proposed that we make Chalmers-Gothenburg’s projects available as well. Both UTokyo's and Chalmers-Gothenburg's projects have an impact on society, but they are complex and difficult for the public to understand. Being able to display the projects on our easy-to-understand and easy-to-use Genochemy would be a very important step forward in explaining the projects to the public in a way that is easy to understand.

What we got

The Chalmers-Gothenburg team introduced the Worm-like chain model to us and gave us the MATLAB code to run the simulation. The Worm-like chain model is a model that considers DNA or proteins as chains and predicts their behavior probabilistically. The Chalmers-Gothenburg team applied it to the linker connecting dCas9 and TEV and used it to calculate the probability that a TEV split in two will unite together; the UTokyo team applied it to the DNA between two recognition sites of recombinase and used it to calculate the probability that recombination will occur. We simulated how the probability of the target sequences coming close enough to each other for recombination, depends on the sequence length of the DNA. In the process, we received feedback that if the DNA was too short, it would be difficult to bend. As a result, we found that the probability of recombination is proportional to about the -1.38 power of the DNA sequence length, and how the probability of recombination increases as the base length is shortened. This improved the problem that the deciphering did not progress even with sufficient exposure to light, which had been a problem throughout the project.

Figure 3

Figure 3. Relationship between the length of sequences between recognition sequences and the probability that two recombinases come close to each other (both logarithmic).