iGEM 2021 — Team UCDavis

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:UCDavis/Notebook

Notebook | iGEM Project Cargo

[![](https://static.igem.org/mediawiki/2021/6/68/T--UCDavis--img--Cargo.png)](https://2021.igem.org/Team%3AUCDavis)

* Team

  [Team](https://2021.igem.org/Team%3AUCDavis/Team)[Attributions](https://2021.igem.org/Team%3AUCDavis/Attributions)[Collaborations](https://2021.igem.org/Team%3AUCDavis/Collaborations)
* Project

  [Contribution](https://2021.igem.org/Team%3AUCDavis/Contribution)[Description](https://2021.igem.org/Team%3AUCDavis/Description)[Design](https://2021.igem.org/Team%3AUCDavis/Design)[Engineering](https://2021.igem.org/Team%3AUCDavis/Engineering)[Drylab](https://2021.igem.org/Team%3AUCDavis/Drylab)[Protocols](https://2021.igem.org/Team%3AUCDavis/Protocols)[Notebook](https://2021.igem.org/Team%3AUCDavis/Notebook)[Modeling](https://2021.igem.org/Team%3AUCDavis/Modeling)[Implementation](https://2021.igem.org/Team%3AUCDavis/Implementation)[Results](https://2021.igem.org/Team%3AUCDavis/Results)
* Parts

  [Part Collection](https://2021.igem.org/Team%3AUCDavis/Part_Collection)[Proof Of Concept](https://2021.igem.org/Team%3AUCDavis/Proof_Of_Concept)
* [Safety](https://2021.igem.org/Team%3AUCDavis/Safety)
* Human Outreach

  [Educational Outreach](https://2021.igem.org/Team%3AUCDavis/Educational_Outreach)[Human Practices](https://2021.igem.org/Team%3AUCDavis/Human_Practices)[Responsible Science](https://2021.igem.org/Team%3AUCDavis/Responsible_Science)
* Supplemental

  [Vaccine Tech](https://2021.igem.org/Team%3AUCDavis/Vaccine_Tech)[Immunology](https://2021.igem.org/Team%3AUCDavis/Immunology)[Disease Modeling](https://2021.igem.org/Team%3AUCDavis/Disease_Modeling)
* [Medal](https://2021.igem.org/Team%3AUCDavis/Medal)

3 Minute Read

# Notebook

Our Bench Work Documentation

by Jason Hu

---

##### Contents

# Full Notebook

Sample template built using the iGEM Wiki Starter Pack by BITS Goa.

Code released under the MIT license.

Based on [Bootstrap](https://getbootstrap.com) and themes [Flatly](https://bootswatch.com/flatly/) and [Darkly](https://bootswatch.com/darkly/) from [Bootswatch](https://bootswatch.com/).

Some content from the [iGEM Example Wiki](https://2020.igem.org/Team%3AExample). Images from [Unsplash](https://unsplash.com). Web fonts from [Google](https://fonts.google.com).

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:UCDavis/Engineering

Engineering | iGEM Project Cargo

[![](https://static.igem.org/mediawiki/2021/6/68/T--UCDavis--img--Cargo.png)](https://2021.igem.org/Team%3AUCDavis)

* Team

  [Team](https://2021.igem.org/Team%3AUCDavis/Team)[Attributions](https://2021.igem.org/Team%3AUCDavis/Attributions)[Collaborations](https://2021.igem.org/Team%3AUCDavis/Collaborations)
* Project

  [Contribution](https://2021.igem.org/Team%3AUCDavis/Contribution)[Description](https://2021.igem.org/Team%3AUCDavis/Description)[Design](https://2021.igem.org/Team%3AUCDavis/Design)[Engineering](https://2021.igem.org/Team%3AUCDavis/Engineering)[Drylab](https://2021.igem.org/Team%3AUCDavis/Drylab)[Protocols](https://2021.igem.org/Team%3AUCDavis/Protocols)[Notebook](https://2021.igem.org/Team%3AUCDavis/Notebook)[Modeling](https://2021.igem.org/Team%3AUCDavis/Modeling)[Implementation](https://2021.igem.org/Team%3AUCDavis/Implementation)[Results](https://2021.igem.org/Team%3AUCDavis/Results)
* Parts

  [Part Collection](https://2021.igem.org/Team%3AUCDavis/Part_Collection)[Proof Of Concept](https://2021.igem.org/Team%3AUCDavis/Proof_Of_Concept)
* [Safety](https://2021.igem.org/Team%3AUCDavis/Safety)
* Human Outreach

  [Educational Outreach](https://2021.igem.org/Team%3AUCDavis/Educational_Outreach)[Human Practices](https://2021.igem.org/Team%3AUCDavis/Human_Practices)[Responsible Science](https://2021.igem.org/Team%3AUCDavis/Responsible_Science)
* Supplemental

  [Vaccine Tech](https://2021.igem.org/Team%3AUCDavis/Vaccine_Tech)[Immunology](https://2021.igem.org/Team%3AUCDavis/Immunology)[Disease Modeling](https://2021.igem.org/Team%3AUCDavis/Disease_Modeling)
* [Medal](https://2021.igem.org/Team%3AUCDavis/Medal)

3 Minute Read

# Engineering

Come See what we made!

by Cuahomatec

---

##### Contents

# TOP, IRES, Unreg Plasmids

To demonstrate how translational rates will be quantified in our IRE plasmids, Dr. John Albeck provided three plasmids with regulatory sequences in the 5’UTR region. These plasmids are TOP-H2B-DD-YFP, IRES-H2B-DD-YFP, and Unreg-H2B-DD-Cardinal. Each of these plasmids contain reporter genes, which allow easy translational quantification through microscopy, H2B domains which help organize eukaryotic DNA, and a destabilization domain that has a high induction ratio once it is stabilized by its ligand trimethoprim (TMP) (Iwamoto et.al 981). TOP is a 5-15 long sequence that encodes translational regulatory components (Han et.al 86). When exposed to the mTOR inhibitor Torin 2, TOP will downregulate translation rates. However, IRES and Unreg translational rates should be unaffected. TOP will be used as a substitute for our own plasmids with IRE sequences in them as they too should theoretically slow translation rates in the presence of iron regulatory proteins. IRES is an internal ribosome entry site that when exposed to dithiothreitol (DTT) translational rates should increase. TOP and Unreg translational effects are unaffected (Han et.al 87). None of our own plasmids contain regulatory elements that increase translational rates, but IRES will be used to contrast unregulated and downregulated translational rates. Unreg has no regulatory elements in the 5’ UTR region which means its translational rate will be uninhibited. Thus, its use will be to model our control plasmid that has no IRE sequence in its 5’ UTR sequence. To test these translational rates, a 96-well glass bottom plate pretreated with collagen type I was seeded with 10k CHO DG-44 cells in each of the inner 60 wells. All wells received media to ensure that the inner 60 did not dry out. After allowing the cells to proliferate for a day in the 37℃ CO2 incubator, all three plasmids were transfected into thirty wells each. Lipofectamine 3000 Reagent from Invitrogen was used and their transfection protocol was followed. The 96 well plate was set up as follows.

![bpdists](https://static.igem.org/mediawiki/2021/f/fd/T--UCDavis--img--eng1.png)

Figure 1: 96 Well Plate Map (2nd Run)

Transfected cells were immediately loaded into a closed 37℃ chamber at 5% CO2 concentration. For the first round of imaging, inhibitors DTT and Torin2 were not added to confirm that translational rates are equal among the three plasmids in the absence of these molecules. Images were collected with a Nikon (Tokyo, Japan) 20/0.75 NA Plan Apo objective on a Nikon Eclipse Ti inverted microscope, equipped with a Lumencor SOLA or Lumencor SPECTRA X light engine. Fluorescence filters used in the experiment are: DAPI (custom ET395/25x - ET460/50 m - T425lpxr, Chroma), YFP (49003, Chroma), Cy5 (49006, Chroma).

![bpdists](https://static.igem.org/mediawiki/2021/f/f7/T--UCDavis--img--eng2.png)

Figure 2: Order in which cells were imaged 1-61

# Treatment 1

![bpdists](https://static.igem.org/mediawiki/2021/5/51/T--UCDavis--img--eng3.png)

Figure 3: Treatment with TMP at the 5.8 hour mark

Images were taken on a 6 minute interval for 17.5 hrs. At the 5.8 hr mark, TMP was added to each of the inner 60 wells at 2mM and 1mM concentrations and DMSO was added to the first column as shown in Treatment 1. Half of the IRES, TOP, and Unreg transfected cells received 1mM TMP whilst the other half received 2mM TMP. After background subtraction and flat field correction, image data were processed to segment and average pixels within each identified cell’s nucleus and cytoplasm, using a custom procedure written for MATLAB (Pargett et al., 2017), Image data was stored in ND2 files generated by NIS Elements and accessed using the Bio-Formats MATLAB toolbox. Individual cells were tracked over time using uTrack 2.0 (Jaqaman et al., 2008). 175 images were generated for each well and the ones that had the best transfection efficiency were exported as movies. To see the translational rates in graph format, see the Modeling page.

# Cell Movies (TMP Added at 5.8 Hours)

[

](https://static.igem.org/mediawiki/2021/b/bc/T--UCDavis--Unreg_0.15_lipo_well3.mp4)

Unreg Well #3 0.15 Microliters Lipofectamine

[

](https://static.igem.org/mediawiki/2021/1/1d/T--UCDavis--Unreg_0.3_lipo_well18.mp4)

Unreg Well #18 0.3 Microliters Lipofectamine

[

](https://static.igem.org/mediawiki/2021/3/30/T--UCDavis--Top_0.15_lipo_well28.mp4)

Top Well #28 0.15 Microliters Lipofectamine

[

](https://static.igem.org/mediawiki/2021/b/b8/T--UCDavis--Top_0.3_lipo_well40_%281%29.mp4)

Top Well #40 0.3 Microliters Lipofectamine

[

](https://static.igem.org/mediawiki/2021/7/7c/T--UCDavis--IRES_0.15_lipo_well50.mp4)

IRES Well #50 0.15 Microliters Lipofectamine

[

](https://static.igem.org/mediawiki/2021/f/fc/T--UCDavis--IRES_0.3_lipo_well52.mp4)

IRES Well #52 0.3 Microliters Lipofectamine

The images shown above are an engineering success as we were able to qualitatively and quantitatively measure translational rates. When our own plasmids are constructed we will be able to accurately measure translational rates.

# Notes

The first run using the microscope was unsuccessful as the program crashed halfway through. The current data is a second attempt at imaging transfected cells without the addition of Torin2 and DDT. Next steps are to complete two more imaging collections, one in which Torin is added to all wells, and another in which DTT is added to all wells. Results should show an increase in translation in

# References

Han, Kyuho, et al. “Parallel Measurement of Dynamic Changes in Translation Rates in Single Cells.” Nature Methods, vol. 11, no. 1, 2013, pp. 86–93. Crossref, doi:10.1038/nmeth.2729. Iwamoto, Mari, et al. “A General Chemical Method to Regulate Protein Stability in the Mammalian Central Nervous System.” Chemistry & Biology, vol. 17, no. 9, 2010, pp. 981–88. Crossref, doi:10.1016/j.chembiol.2010.07.009. Jaqaman, K., Loerke, D., Mettlen, M., Kuwata, H., Grinstein, S., Schmid, S.L., and Danuser, G. (2008). Robust single-particle tracking in live-cell time-lapse sequences. Nat. Methods 5, 695–702. Pargett, M., and Albeck, J.G. (2016). Mapping the spectrum of gene expres- sion responses. Cell Syst. 2, 221–222.

Sample template built using the iGEM Wiki Starter Pack by BITS Goa.

Code released under the MIT license.

Based on [Bootstrap](https://getbootstrap.com) and themes [Flatly](https://bootswatch.com/flatly/) and [Darkly](https://bootswatch.com/darkly/) from [Bootswatch](https://bootswatch.com/).

Some content from the [iGEM Example Wiki](https://2020.igem.org/Team%3AExample). Images from [Unsplash](https://unsplash.com). Web fonts from [Google](https://fonts.google.com).

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:UCDavis/Experiments

Experiments | iGEM Project Cargo

[![](./assets/img/Cargo.png)](https://2021.igem.org/Team%3AUCDavis)

* Team

  [Team](https://2021.igem.org/Team%3AUCDavis/Team)[Attributions](https://2021.igem.org/Team%3AUCDavis/Attributions)[Collaborations](https://2021.igem.org/Team%3AUCDavis/Collaborations)
* Project

  [Contribution](https://2021.igem.org/Team%3AUCDavis/Contribution)[Description](https://2021.igem.org/Team%3AUCDavis/Description)[Design](https://2021.igem.org/Team%3AUCDavis/Design)[Engineering](https://2021.igem.org/Team%3AUCDavis/Engineering)[Drylab](https://2021.igem.org/Team%3AUCDavis/Drylab)[Notebook](https://2021.igem.org/Team%3AUCDavis/Notebook)[Modeling](https://2021.igem.org/Team%3AUCDavis/Modeling)[Implementation](https://2021.igem.org/Team%3AUCDavis/Implementation)[Results](https://2021.igem.org/Team%3AUCDavis/Results)
* Parts

  [Part Collection](https://2021.igem.org/Team%3AUCDavis/Part_Collection)[Proof Of Concept](https://2021.igem.org/Team%3AUCDavis/Proof_Of_Concept)
* [Safety](https://2021.igem.org/Team%3AUCDavis/Safety)
* Human Outreach

  [Educational Outreach](https://2021.igem.org/Team%3AUCDavis/Educational_Outreach)[Human Practices](https://2021.igem.org/Team%3AUCDavis/Human_Practices)[Responsible Science](https://2021.igem.org/Team%3AUCDavis/Responsible_Science)
* Supplemental

  [Vaccine Tech](https://2021.igem.org/Team%3AUCDavis/Vaccine_Tech)[Immunology](https://2021.igem.org/Team%3AUCDavis/Immunology)[Disease Modeling](https://2021.igem.org/Team%3AUCDavis/Disease_Modeling)
* [Medal](https://2021.igem.org/Team%3AUCDavis/Medal)

# Experiments

Code

---

##### Contents

Sample template built using the iGEM Wiki Starter Pack by BITS Goa.

Code released under the MIT license.

Based on [Bootstrap](https://getbootstrap.com) and themes [Flatly](https://bootswatch.com/flatly/) and [Darkly](https://bootswatch.com/darkly/) from [Bootswatch](https://bootswatch.com/).

Some content from the [iGEM Example Wiki](https://2020.igem.org/Team%3AExample). Images from [Unsplash](https://unsplash.com). Web fonts from [Google](https://fonts.google.com).

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:UCDavis/Results

Results | iGEM Project Cargo

[![](https://static.igem.org/mediawiki/2021/6/68/T--UCDavis--img--Cargo.png)](https://2021.igem.org/Team%3AUCDavis)

* Team

  [Team](https://2021.igem.org/Team%3AUCDavis/Team)[Attributions](https://2021.igem.org/Team%3AUCDavis/Attributions)[Collaborations](https://2021.igem.org/Team%3AUCDavis/Collaborations)
* Project

  [Contribution](https://2021.igem.org/Team%3AUCDavis/Contribution)[Description](https://2021.igem.org/Team%3AUCDavis/Description)[Design](https://2021.igem.org/Team%3AUCDavis/Design)[Engineering](https://2021.igem.org/Team%3AUCDavis/Engineering)[Drylab](https://2021.igem.org/Team%3AUCDavis/Drylab)[Protocols](https://2021.igem.org/Team%3AUCDavis/Protocols)[Notebook](https://2021.igem.org/Team%3AUCDavis/Notebook)[Modeling](https://2021.igem.org/Team%3AUCDavis/Modeling)[Implementation](https://2021.igem.org/Team%3AUCDavis/Implementation)[Results](https://2021.igem.org/Team%3AUCDavis/Results)
* Parts

  [Part Collection](https://2021.igem.org/Team%3AUCDavis/Part_Collection)[Proof Of Concept](https://2021.igem.org/Team%3AUCDavis/Proof_Of_Concept)
* [Safety](https://2021.igem.org/Team%3AUCDavis/Safety)
* Human Outreach

  [Educational Outreach](https://2021.igem.org/Team%3AUCDavis/Educational_Outreach)[Human Practices](https://2021.igem.org/Team%3AUCDavis/Human_Practices)[Responsible Science](https://2021.igem.org/Team%3AUCDavis/Responsible_Science)
* Supplemental

  [Vaccine Tech](https://2021.igem.org/Team%3AUCDavis/Vaccine_Tech)[Immunology](https://2021.igem.org/Team%3AUCDavis/Immunology)[Disease Modeling](https://2021.igem.org/Team%3AUCDavis/Disease_Modeling)
* [Medal](https://2021.igem.org/Team%3AUCDavis/Medal)

4 Minute Read

# Results

What we discovered and invented, but more importantly what we want to find next.

by Jason Hu

---

##### Contents

The pandemic has created a unique situation for science. We only had lab access starting the middle of July and acquiring funding became a new challenge with the pandemic’s effect on the general financial situation. Regardless, the students of UC Davis took this challenge and continued to pursue discovery. Below is a summary of our findings/advancements from our Dry Lab, Integrated Human Practices, and Wet Lab.

# Dry Lab

Project Cargo created a functioning computational pipeline based on the ViennaRNA package that is able to take an RNA secondary structure in a fasta format and iterate through an RNA sequence to find the best insertion location. This RNA sequence should be broken up into the RNA secondary structure of interest, 5’ UTR, and coding sequence. From here it takes the RNA secondary structure, iterates the secondary structure at every position of the 5’ UTR to generate a Unique sequence. All these sequences are stored in a single fasta file. That comprehensive fasta file is then fed into fold-fast-alike, a piece of code provided by Dr. Radecki, which parses the fasta file and creates base pair probabilities for each sequence that gets stored as individual folders.

To determine the best candidates, we parse their base pair probabilities to see how likely an RNA structure is likely to fold and not conflict with native RNA structures. We also look at the dot-plot graph generated also by fold-fast-alike and allows us to observe general folding trends to select out candidates with the possibility of global folding resulting in a piece of RNA that will “kiss” onto itself and compromise biological function.

From our folding simulations, we determined that position 17 and 35 were stable locations in the 5’ UTR to place our IRE for our constructs. We also had access to the Moderna and Pfizer-BioNTech mRNA vaccine sequence. We determined

To validate our folding pipeline we compared it to nature and took wildtype ferritin heavy chain genes from humans on the NCBI database. We removed the highly conserved wildtype IRE sequences from and fed those sequences through our pipeline seeing if we would re-insert the IRE back into their native location. We successfully did this with the human ferritin gene where it is naturally found at position 32 and our pipeline predicted position 32 to be most ideal as well. This is under the assumption that natural selection has placed the IRE into a location that is conformationally the most stable.

We took great lengths to document the mechanics of our code and create a comprehensive guide on GitHub for future RNA folding endeavors. We hope that our package can be a useful tool for future iGEM and research purposes.

# Integrated Human Practice

Vaccines with reduced side effects would help people with aging immune systems, chronic conditions, and disabilities achieve immunity while minimizing the side effects. Knowing this we felt simply doing a survey that asked people an almost rhetorical question “if a vaccine had reduced side effects, would you prefer to take that one?” was not productive. We felt instead that it would be better to explore the sources of vaccine hesitancy in the USA in Black and/or disabled people. Historically these communities have had to be more cautious of US medical initiatives due to a history of exploitation or inequity.

We worked very closely with the wonderful Mr. Glenn Ellis to connect us to seven, also wonderful, people who come from the Black 50+ years old community with varying degrees of chronic illness and disability. They came from the states of Alabama and Pennsylvania. The concerns from people of these communities were not fully addressed in the pandemic response and this led to slower vaccine distribution thus a less effective herd immunity.

We conducted phone interviews with these seven individuals. We found stories ranging from delayed vaccine access to impoverished communities to one individual who received one of the rarest side effects from the mRNA vaccine; paralysis. Simply summarizing their stories and experiences here does not do them justice at all. We highly encourage readers to go to our “Human Outreach” Tab to learn and read their stories.

In a brief summary, what we found is that four of the seven people experienced side effects from a mRNA vaccine with three cases that were more severe than previous vaccinations and three people who did not have side effects from either mRNA or previous vaccines. Aside from side effects we had strong leads into what other socioeconomic and historical factors could contribute to low vaccination rates in Black Americans.

Project Cargo believes science is only as valuable as the good it can do for people. So like our RNA folding platform, hopefully we laid down the groundwork for future research efforts.

# Wet Lab

We aimed to verify our mRNA folding pipeline with wet lab data. However, as of currently writing this, we are still working to assemble our plasmid for transfection and we hope to be able to report on our cumulative translation and translation rate for the judging session. We hope to bring exciting results then!

Our goal was to take an IRE that would bind to IRBP1, IRBP2, a mutant IRE that could bind to both, or a mutant that could bind to neither to assess the effect of initial translation rates of our designed mRNA constructs. We aimed to replicate vaccine conditions as closely as possible by utilizing a lipid particle based delivery of our DNA plasmids via lipofectamine into mammalian cells.

Rather than encoding for a spike protein like the real vaccine, our coding region (“cargo”) would have encoded for a membrane integrating protein (VSV-G) fused with an HA tag for HA antibody staining to assess total translation amount. Separated by a P2A cleavage sequence that allows for a single piece of RNA to encode for another protein. Downstream of the P2A site, the translation is stopped and the next sequence begins translation. Our second protein was a destabilized YFP protein that can be induced to fluorescence with the addition of Trimethoprim (TMP).

We would transfect CHO DG44 cells with our various plasmids with the different mutated IRE’s at the locations of 17 and 35 in the 5’ UTR. Transfected cells would be monitored in Nikon Eclipse Ti inverted microscope installed in Dr. Albeck’s lab. This microscope is able to act as both an incubator for mammalian cell growth and measure the change in fluorescence over time with a mobile camera that can capture image data through several different light channels over the course of days. The Cy5 channel were used to monitor mCardinal fluorescence and YFP for the venus fluorescence.

# Note

Just because the wiki freese has passed, that does not mean our work is done! We have a new plan on how to assemble our plasmid for transfection and imaging. We have taken every step we can at the moment to validate all the computational, wet lab protocols, data collection, and plotting methods we need to. Thus we will be ready to collect data as soon as we build our plasmid fully.

Sample template built using the iGEM Wiki Starter Pack by BITS Goa.

Code released under the MIT license.

Based on [Bootstrap](https://getbootstrap.com) and themes [Flatly](https://bootswatch.com/flatly/) and [Darkly](https://bootswatch.com/darkly/) from [Bootswatch](https://bootswatch.com/).

Some content from the [iGEM Example Wiki](https://2020.igem.org/Team%3AExample). Images from [Unsplash](https://unsplash.com). Web fonts from [Google](https://fonts.google.com).