iGEM 2022 — Team mit

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[Link to Notebook](https://static.igem.wiki/teams/4320/wiki/lab-notebook-igem-2022-google-docs-compressed.pdf)

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# Overview

1. Using Modeling Results to Inform Decisions and Experimental Design
2. Troubleshooting Cloning

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# Using Modeling Results to Inform Decisions and Experimental Design

## Our Original Design for GSHield

In our initial conceptualization of GSHield, we only considered the glutathione production at the boundary between the patch and the oral cavity. However, during our meeting with Dr. Stephen Sonis, he explained that damage from oral mucositis usually occurs from the bottom up. Therefore, sequestering ROS at the level of basal epithelial cells would be more effective than a surface-level response.

Thus, this posed a new question:

> Can the glutathione, produced at the surface level, penetrate deep enough into the oral epithelium to affect the basal lamina?

Tight junctions in the oral epithelium break down during cancer therapy, so the penetrability of substances from surface improves over time. Therefore, the idea that glutathione could diffuse from surface to the basal level is possible. However, we were not certain if this would happen.

## Modeling for Tissue Diffusion

In order to improve on our previous plan, we decided to develop a model to study glutathione diffusion through compromised epithelium junctions and determine whether we can achieve therapeutic levels of glutathione in deeper parts of the tissue.

Our model was based on a linear 1D nutrient diffusion in cerebral organoid constructs detailed in a paper by Dr. Richard McMurtrey in 2016. Below represents our calculations to produce the model equation:

We considered the following factors when defining our model:

* **Surface extracellular GSH (glutathione) concentration:** How much GSH is produced by the patch at the surface level? This value was ballparked as we did not yet have data on how much glutathione our patch would produce.
* **Typical extracellular GSH concentration:** How much GSH is typically present in the extracellular matrix?
* **Location:** Where in the mouth is the patch being applied?
* **Metabolic rate for GSH in cells:** What is the rate of consumption of glutathione by cells?
* **Cell density**
* **Diffusion coefficient**
* **Size of patch**

More information on our process to define these factors can be found below:

For some of the above factors such as diffusion coefficient, we had to rely on average/typical values. Other factors that we would like to incorporate in the future include age, sex, body temperature, chemotherapy history (# of cycles, dose, etc), and lifestyle (ex. smoker). Many of these factors would improve our estimate for the diffusion coefficient, thus improving the model; however, we were unable to find suitable data at this time.

Running this model produced a plot of the GSH concentration as a function of depth at a given time. The depths of the stratum basale at different locations in the mouth are marked on the plot. These values were taken from data from Measurement of Oral Epithelial Thickness by Optical Coherence Tomography (Stasio et. al 2019), which measured the thickness of the oral epithelium in non-cancerous subjects. This would have been improved by using data from patients undergoing cancer therapy; however, we found several sources that appeared to have conflicting data and opted to stick with our original source for the time being.

Below is the MATLAB code and snapshot of the plot at t = 15 min, for reference.

Therefore, we now have a way to determine how deep the glutathione will travel, in lieu of experiments on in vivo models. However, this model can continued to be iterated upon.

## Designing New Experiments

Our mathematical model was able to further inform the design of the following experiment to help us further improve the project.

1. **Determine amount of ROS sequestered by GSH:** Now that we have defined the level of GSH at the stratum basale, we can conduct an experiment to determine how much ROS can be sequestered by that concentration of GSH. We would create an environment of liquid media with our desired GSH concentration and then add varying levels of hydrogen peroxide (an ROS) to the media. Finally, we can use an assay to measure the level of ROS sequestered and from that, determine if the concentration of GSH is at a therapeutic level.

Additionally we can use the following experiments to improve upon our model, which can then further inform more experiments.

2. **Determining rate of glutathione production:** Hydrogen peroxide would be added to the functionalized patch at varying concentrations, and we would measure the amount of glutathione produced as the yeast in the patch should be responsive to oxidative stress. With this, we can determine a relationship between ROS and glutathione and improve estimate of surface extracellular GSH concentration in the model.

Our model will also allow us to specify physical design features of the patch, such as the size, balancing a comfortable fit with providing enough surface area of yeast cells to produce enough glutathione.

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# Troubleshooting Cloning

## Design Our Plasmids

Our original goal was to design plasmids to import the acsABCD cellulose synthesis operon into E. coli. We decided to follow the design of the 2014 Imperial College iGEM team; thus, we fused acsAB together in one plasmid and acsC and D with a ribosomal binding site (RBS) in the middle of the two genes. However, IDT had synthesis issues, so we couldn’t order the entire acsC-rbs-acsD part as a single gBlock.

We knew that Golden Gate could assemble up to 10 parts reliably, and ordering multiple gBlocks would allow for faster shipping. So, we designed acsC and D as an assembly of 5 smaller fragments.

## Building and Testing

From here, we followed our existing Golden Gate assembly protocol to build up the plasmid. Next, we tested our plasmid to see if it worked (by running a restriction digest and sequencing). However, we noticed that we kept getting weird assemblies.

## Troubleshooting and Redesign

To move forward and learn from these errors, we consulted our mentors. While reviewing the assembly in Benchling, we found out that one of the sticky ends was too similar. Thus, it could have been assembling in the incorrect order or in an infinite loop, decreasing assembly efficiency. From this, we redesigned our primers to fix these issues with the gBlock.

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# Citations

McMurtrey R. J. (2016). Analytic Models of Oxygen and Nutrient Diffusion, Metabolism Dynamics, and Architecture Optimization in Three-Dimensional Tissue Constructs with Applications and Insights in Cerebral Organoids. Tissue engineering. Part C, Methods, 22(3), 221–249. https://doi.org/10.1089/ten.TEC.2015.0375

Stasio, D. D., Lauritano, D., Iquebal, H., Romano, A., Gentile, E., & Lucchese, A. (2019). Measurement of Oral Epithelial Thickness by Optical Coherence Tomography. Diagnostics (Basel, Switzerland), 9(3), 90. https://doi.org/10.3390/diagnostics9030090

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# iGEM 2022 Master Protocols Document

[MIT iGEM 2022 Master Protocols Document](https://static.igem.wiki/teams/4320/wiki/2022-mit-igem-protocols-google-docs.pdf)

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Here we share a few key results that we accomplished over the course of the summer as well as some ideas about experiments that we will pursue in the future.

# Cloning using the Dueber YTK Toolkit

As we wanted to express different genes such as mRuby2, ADP1, and GSH1 under the CCP1 promoter, we decided to use a modular cloning method to assemble our plasmids so we could swap out parts easily. We ultimately decided to use the MoClo Yeast ToolKit (YTK) is a collection of 96 standardized and characterized *S. cerevisiae* parts that can be used for modular and multi-part assembly of DNA constructs (Lee et. al 2015).

We were fortunate enough to receive a copy of the YTK from Zijay Tang, a research fellow at the Wyss Institute who also gave us advice on the design of our *S. cerevisiae*S. cerevisiae and *E. coli* co-culture patch design and used the same tool kit in the construction of his Syn-SCOBY system (Gilbert et. al 2021).

Using the level 0 backbone pYTK\_001 (BBa\_K4320006), we were able to successfully assemble parts BBa\_K4320000 through BBa\_K4320005 into level 0 expression plasmids which we cloned in *E. coli* and sequence verified using Plasmidsaurus full plasmid sequencing services.

We then moved on to the construction of level 1 parts, BBa\_K4320021 through BBa\_K4320026, using the level 1 backbone pYTK\_096 (BBa\_K4320007), as well as pYTK\_097 (BBa\_K4320008) and pYTK\_098 (BBa\_K4320009) which were backbones we assembled using parts from the YTK for transformation into *S. cerevisiae*. However, ultimately we did not have enough time to completely assemble and verify these parts.

# Cloning *E. coli* parts

We were able to verify the pJUMP26-1A plasmid, which included superfolder GFP (sfGFP) from the iGEM distribution kit using full-plasmid sequencing from Plasmidsaurus.

We, unfortunately, did not have much luck cloning our *E. coli* parts as we had large gene sequences that needed to be synthesized. However, we did successfully clone and verify our level 0 acsAB, which was a 3-part + vector assembly of multiple gBlocks using the Golden Gate Assembly method. We transformed acsAB into NEB 5-alpha competent *E. coli* cells, screened for cells based on antibiotic resistance, and verified its sequence by full-plasmid sequencing through Plasmidsaurus.

We plan to try to assemble acsC-RBS-acsD again. We hypothesize that the low assembly efficiency is due to the fact that we are attempting a 5-part + vector assembly and have yet to optimize Golden Gate conditions. We plan to continue trying to find optimal conditions for our assembly.

# Cloning *S. cerevisiae* parts

We successfully cloned and verified most of our level 0 parts, including CCP1 (BBa\_K4320000), ADP1 (BBa\_K4320002), GSH1 (BBa\_K4320003), and cyc1 (BBa\_K4320005). To accomplish this, we used Golden Gate Assembly to assemble our parts and transformed them into NEB 5-alpha competent*E. coli*E. coli cells, screened for cells based on antibiotic resistance, and verified their sequence by full-plasmid sequencing through Plasmidsaurus. We were also able to verify many of the Dueber YTK Toolkit parts to be used for building our level 1 parts including pYTK002 (ConLS), pYTK032 (mTurquoise2), pYTK053 (tADH1), pYTK067 (ConR1), pYTK075 (LEU2), and pYTK083 (AmpR-ColE1). We did this by assembling a plasmid of many of these parts, transforming this plasmid into NEB 5-alpha competent*E. coli*E. coli cells, screened for ampicillin resistance, and verified its sequence by sequencing.

# Transforming into *S. cerevisiae*

We successfully performed several repeats of a *S. cerevisiae* transformation of a test plasmid from the YTK toolkit, such as pYTK096 (BBa\_K4320007​​) from the Dueber YTK Toolkit. This plasmid was successfully transformed into the common *S. cerevisiae* working strain BY4742.

However, we failed to clone other plasmids into *S. cerevisiae*. We tried these transformations once and plan to try again. Assuming our constructs are correct, we hypothesize that our transformation conditions are not optimal, particularly as we noted that cell density on our control plates was very high.

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# Citations

Lee ME, Deloache WC, Cervantes B, Dueber JE (2015). A Highly Characterized Yeast Toolkit for Modular, Multipart Assembly. ACS Synth Biol. 4(9):975–986. <https://doi.org/10.1021/sb500366v>

Gilbert C, Tang TC, Ott W, Dorr BA, Shaw WM, Sun GL, Lu TK, Ellis T (2021). Living materials with programmable functionalities grown from engineered microbial co-cultures. Nat. Mater. 20, 691–700. <https://doi.org/10.1038/s41563-020-00857-5>

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