iGEM 2021 — Team OhioState

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# Team:OhioState/Notebook

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[Contribution](https://2021.igem.org/Team%3AOhioState/Contribution)
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[Experiments](https://2021.igem.org/Team%3AOhioState/Experiments)
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[Parts](https://2021.igem.org/Team%3AOhioState/Parts)

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[Model](https://2021.igem.org/Team%3AOhioState/Model)

Human Practices▼

[Communication](https://2021.igem.org/Team%3AOhioState/Communication)

[Human Practices](https://2021.igem.org/Team%3AOhioState/Human_Practices)
[Regulations](https://2021.igem.org/Team%3AOhioState/Regulations)

Results▼

[Results](https://2021.igem.org/Team%3AOhioState/Results)
[Contribution](https://2021.igem.org/Team%3AOhioState/Contribution)
[Implementation](https://2021.igem.org/Team%3AOhioState/Implementation)
[Proof of Concept](https://2021.igem.org/Team%3AOhioState/Proof_Of_Concept)

#

# Notebook

Below you will find an overview of the major deliverables, decisions, and brainstorming from each week of our project timeline! Each date is for the week of.

April

## Wet Lab

#### 03/03/21

- Bacterial-induced sepsis is the overreaction to a lipid A, a portion of an endotoxin in the bacterial cell membrane. This could be targeted using synthetic biology techniques to neutralize the harmful effects.
- Our plan was to insert a phage vector containing a trp promoter into E.coli until we want it to be lytic

## Dry Lab

## HP

#### 02/01/21

- Partnered with SJMS to design 5 days of summer camp materials

#### 03/01/21

- Hosted a global meet-up on zoom with over 23 teams present. This allowed teams to swap project ideas and collaborations

#### 04/20/21

- Presented our project pitch to ID SCOPE (At the Ohio State University)

May

## Wet Lab

#### 05/03/21

- Decided to use FraR so that the system will be off in our lab E. coli and on in wild E. coli to prevent expression of toxic genes.

## Dry Lab

#### 05/10/21

- Decided which parts of the Model criteria we might be able to fill

#### 05/17/21

- Looked into modeling in general
- Discussed emailing an OSU professor who teaches modeling (Dr. Friedman) as well as the William & Mary iGEM team as they won best model last year

#### 05/24/21

- Discussed the general idea of models
- Made sure everyone had Matlab downloaded
- Emailed William & Mary iGEM team as they won best model last year for ideas on how to structure our modeling plan

## HP

#### 05/10/21

- Discussed medal criteria options
- Discussed timeline and expected progress in this summer
- Start brainstorming for summer camp ideas

#### 05/17/21

- Present summer camp ideas in committee meeting these included a strawberry DNA lab and a transformation lab
- Met with Evan Boylan from SJMS to update him on our summer camp plans

#### 05/24/21

- Committee members presented their own summer camp activities and suggestions (i.e. epidemiology experiment along with how to write up the protocols)
- Organize summer camp schedule and materials. We have 7 different protocols to divide over 5 days.
- Ryan tested the Strawberry DNA lab at home and found it was successful even without a strainer as long as most of the strawberry chunks did not get into the final solution.

June

## Wet Lab

#### 06/14/21

- Met Dr. Gunn and was advised to get rid of eukaryotic protein modifier for lipid A due to difficulty of expression

#### 06/28/21

- Cloned Promoters in TOPO

## Dry Lab

#### 05/31/21

- Dove into more detailed model ideas including genetic circuit, enzyme kinetics (divvied up each enzyme to members to find kinetics), and start to end model of a phage therapy treatment
- Discussed professionals to reach out to and had email drafts due at the next meeting

#### 06/07/21

- Reviewed email drafts
- No one found kinetics of their enzyme binding to lipid A. We were advised it would be difficult to calculate and test kinetics in lab (that would defeat the purpose of modeling anyways)
- Erin introduced the idea of using docking to investigate the best protein to use
- Everyone will try to find crystal structures of their respective proteins to be used in docking

#### 06/14/21

- Crystal structures were found for most of the proteins
- Satvik and Ryan met on June 20th and came up with a plethora of model ideas. Marked plausible and pleasant ones in the meeting this week
- Each member was assigned a model to look more in depth into

#### 06/28/21

- Met with Austin Cool, a graduate student who works with computational biology specifically with docking. We learned that Docking scores cannot be compared between different proteins with the same molecule. So, we can’t compare our binding proteins, but we could compare a single protein and different molecules.
- For the binding proteins we could generate contact maps which show the parts of the protein binding to the molecule. This could show important binding interactions that make a good/strong lipid A binder
- Presented model proposals: Catheter, Bacteria evolving to be phage resistant, Phage database, Phage therapy modeling with immune system, Docking using crystal structures

## HP

#### 05/31/21

- Continued presenting summer camp activities to HP committees
- Discuss summer camp logistics

#### 06/07/21

- Host summer camp at SJMS
- Met with FCB\_UANL iGEM to discuss a collaboration where we expand on our regulations work from last year.

#### 06/14/21

- Brainstormed interviewing patients of sepsis and physicians to target communities and professional perspective
- Met with MSU iGEM to offer our advice from our experience working with regulatory work and biocontainment last year.

#### 06/28/21

- Met with FCB\_UANL (regulation) to discuss how phage therapy could be implemented into other countries, and determine what regulations hinder application of engineered phage therapy
- Met with Rochester (children book). Planning on running a panel together during Sepsis Awareness month. We also want to create a children’s book about sepsis. They are also reaching out to physicians and patients so we thought it would be a great opportunity to exchange notes.

July

## Wet Lab

#### 07/5/21

- PCR Verification of the Clones
- Digestion of TOPO clones
- Ligation into reporter plasmid

## Dry Lab

#### 07/5/21

- Discussed model ideas and narrowed it down to catheter, genetic circuit, docking, and phage database
- Completed a quick worksheet for next meeting outlining, inputs, outputs, what medal criteria the model will meet, and how the model will help the overall project
- Went over more wet lab concepts on how the phage will actually be working included learning that we have proteins that competitively bind to lipid-A and some that modify it.
- Finalized the above 4 selected models
- Met with William and Mary iGEM to gain their perspective on how to set up a good modeling project. We found they distribute the work to a primary person, and everyone has a secondary model they help out with. This is similar to how we have been doing it.

#### 07/12/21

- Started models and talked about any roadblocks or questions
- Set up format of meetings in the future, everyone bringing an update presentation of the work they have done and where they are
- Creating github accounts so Lizzie can share her code with us

#### 07/19/21

- Database: get units for columns and reach out to phage database to see how they gathered everything
- Docking information: Make colorful, neat, and easier to see
- Genetic Circuit: Look into low, medium, and high rate ranges
- Catheter: keep looking into catheter designs

#### 07/26/21

- Database: keep getting units and information to add
- Docking: Meet with Austin about docking and attempt an actual simulation
- Catheter replacement: look into the business avenue instead of catheter
- Met with Dr. Mandell, who mentioned that docking would not give useful outputs due to the approximations required.
- Officially dropped catheter idea after meeting with Dr. Adkins and Dr. Mandell
- Brainstormed other outside excellence ideas
- Dr. Strathdee recommended a database that included receptors of each of the phage targets.

## HP

#### 07/12/21

- Outreach team: reach out to Dr. Crouser and one patient related to a team member
- Regulation team: Continue revising and meeting with FCB\_UANL to discuss regulation proposal

#### 07/19/21

- Outreach team: Updated email content with Dr. Crouser and Dr. Adkins
- Decide library meet up time for children’s book idea
- Communicate with Rochester that each of the teams will write-up a draft for the storyline.
- Met with Dr. Adkins, a physician in the ICU. We found that a lot of ICU cases involve sepsis, and catheters are a huge risk. However catheters are too off topic from our project so we have decided not to pursue this project further.

#### 07/26/21

- Dr. Mandell encouraged our children’s book idea saying “a good way to appeal to adults is through their children”. He also recommended reaching out to the FDA for our regulations work.
- Met with Dr. Strathdee and Dr. Patterson, the author and patient of the book “The Perfect Predator”. Dr. Strathdee also mentioned contacting someone at the FDA to gain insight into our regulations work.

August

## Wet Lab

- Isolated digested inserts in preparation of phage cloning
- Continued to characterize promoters
- Due to COVID-19 delays, the phage cloning kit won’t be here for a couple months. We decided to attempt traditional phage cloning

## Dry Lab

#### 08/2/21

- Gene circuit: waiting on wet lab data to flush out our model
- Database: still being filled out, looking into professional database options
- Bacteria model: reading and deciphering the paper Dr. Vatsan sent us

#### 08/16/21

- Met with Dr. Debnath to discuss model ideas. He confirmed that docking is not an effective route for a model. And recommended a bacterial mutation rate model instead.
- Dr. Debnath also suggested ordering the database on the bugs that are most common or popular.
- Dr. Raman supported pursuing a model between phage and bacteria and how they evolve together while comparing the effect on various conditions. He thought a mutation model would be less effective due to the high random chance.

## HP

#### 08/9/21

- Dr. Debnath recommended regulations incorporating aspects such as upscaling and cost.

#### 08/16/21

- Met with Brooke Zentmeyer, an OSU law student to discuss our regulations work. She proposed structuring it like a petition with parts that include action items and an objective.

September

## Wet Lab

#### 09/13/21

- Isolated unmodified phage vector
- Digested phage DNA
- Ligation in phage clones
- Unsuccessfully screened for phage clones

## Dry Lab

#### 08/30/21

- Continued work on the phage database

#### 09/13/21

- Met with William and Mary iGEM team who recommended looking into epidemiology models (SIR compartmental model) to address why our population control modeling is not showing the expected results.
- Worked towards getting experimental data for the genetic circuit model

#### 09/20/21

- Made progress on the phage-bacteria system

## HP

#### 09/13/21

- Completed the illustrations for the children’s book to be ready for review by friends and family
- Presented at Rochester Symposium to raise awareness on Sepsis

October

## Wet Lab

- Switched to backup plan and lysed clones with anti-lipid A proteins expressed
- Tested clone lysates on LAL Assay
- Unsuccessful cloning continued

## Dry Lab

- Met with Dr. Rathman to check to make sure we correctly coded the ODE. Also discussed how to handle the S prime and P prime terms in the system of ODEs
- Talked with Dr. Raman about the prime terms and ways to get around them
- Worked with Dr. Rathman again to come up with a way around the prime terms using a mass matrix
- Finalized model using a steady state assumptions and outputted graphs

## HP

## Team Operations Timeline

Below you will find our yearly project timeline and materials we used to keep our team organized. We hope that future teams can use this information to structure there teams in a way to complete their project on time and maintain an organized team.

| Time Period | Action | What happens | Purpose |
| --- | --- | --- | --- |
| December | Recruitment | Create google survey of an application that includes name, major, year, and an essay that shows literature reading and writing capabilities and creative ideas | Limits the number of applicants so we can find the individuals best fit for our team |
| Post application on social media, ask professors to send it out | Promotes the team existence and campus awareness |
| Deadline is due first Sunday of January, the following week will be hosting interviews for those whose applications matched our criteria | Practices professional development and simulates an actual job interview. Also an opportunity to meet the applicants face to face |
| January | Meet the team | Host interviews and our first meeting. This time is used to meet the team and gather necessary information. We also introduce them to our platforms we use: Slack, google drive, zotero, and benchling | An introduction to keep our team organized and everyone knows how to communicate and save research findings |
| February | Welcome to iGEM | Medal Criteria overview | An introduction to the vast idea of iGEM, which is a lot to explain to someone who has no background in it. |
| Past project presentation | This is our first 'assignment' where we pair a new member with a returning member as a sort of mentor/mentee system. This allows new members to explore what previous teams have done and to start generating ideas for our project. Also practices professional development presentation skills. Also gives members a personal connection to someone on the team which is especially hard because we were meeting online |
| Brainstorm | After getting a deeper understanding of what iGEM is and what can be done, we begin to brainstorm. We start by having students look for problems that could be addressed, interesting things about science, and anything else that just gets ideas out there. We will then begin to research and narrow down our options by determining what's feasible and what is the largest issue at hand. | Get ideas down for possible iGEM projects to look into |
| March/April | We narrow down our choices based on everyone's interest. This year we had everyone draft a proposal that included project background, what we would do, and what impact this project would have. We then take the projects that members wrote their proposal on and vote to narrow down to 2 or 3 project ideas. | Determine who is interested in what and to see which ones are our top choices |
| After splitting the team based on interest for our top 2 or 3 project ideas, we spend the next month meeting with professionals and finding more research that supports the feasibility and purpose of our top project choices. To conclude this phase teams will submit a written proposal (more in depth that before), project timeline, and create a project presentation that is presented to our entire team and the attendees of an Infectious Disease SCOPE meeting at the Ohio State Battelle Center | Go really in depth into our top choices to see which one is best |
| May | Project | School has ended and we've voted on our top project choice from the presentations taking into advice from our advisors and ID scope. We then split our team into committees (wet, dry, HP, visual) and go more in depth to planning out our summer |  |
| Summer | Meetings | We have a weekly general meeting where everyone attends to discuss team progress and expectations, what is happening in each committee, and then we have a discussion on any troubles each commiittee is having or if they have any work to present |  |
| Each committee will have meetings based on material (wet lab generally has 2), Dry and HP has 1, and visual tends to be every other week |  |
| Expectations | To keep everyone accountable and on top of things, and as a way to check our team progress and monitor where everyone's time is going we have everyone filling out weekly timesheets that include hyperlinks to the materials they worked on, a short description, and the hours they spent on that item |  |
| Useful documents | Common documents that we have used throughout the summer are the timesheet spreadsheet where you can download the excel template [here](https://static.igem.org/mediawiki/2021/3/3b/T--OhioState--21TimesheetTemplate.xlsx) (we converted ours to a google spreadsheet to host on our shared Drive). You can also download an the Agenda template we used for our general meetings [here](https://static.igem.org/mediawiki/2021/7/76/T--OhioState--21AgendaTemplate.pdf). As a large part of the iGEM competition is documenting our project, we have created an excel file that keeps everyone accountable to have an even share in the write-ups. This spreadsheet automatically marks cells when an assignment is late, and records how many write-ups each member has signed up for. This can be downloaded [here](https://static.igem.org/mediawiki/2021/c/c2/T--OhioState--21PeerEditTemplate.xlsx). Once downloaded it would need to be converted into a google spreadsheet file and the cells with FALSE should be changed to a check box. |  |

Below are examples of our useful documents that we have used to keep our project deliverables organized and no information is lost. The timesheets were especially helpful in keeping track of what each member is working on and how much time spent on each item. This helps make sure we are spending our time where it needs to be. The excel file makes seeing where are time is going easily because it automatically compiles everyone's hours into one graph, but one could still go into each person's timesheet to find the links to the work they focused on that week.

We used the agenda template shown below for our general meetings to keep our meetings organized, on track, and to avoid loosing any ideas or topics addressed at the meetings. We recommend including an agenda that contains topics to be addressed and a action items list to keep everyone aware on what to focus on for that week.

Lastly, we organized a spreadsheet to make dividing the wiki documentation easier. We value this experience of learning how to document the project's progress and wanted to ensure each member got experience! The spreadsheet contains automatic functions that tally how much a person has signed up for, and will turn the cell red when an assignment is marked late.

![Timesheet](https://static.igem.org/mediawiki/2021/0/0b/T--OhioState--21TimesheetExample.svg)
![Agenda](https://static.igem.org/mediawiki/2021/9/9f/T--OhioState--21AgendaThumbnail.png)
![Peer](https://static.igem.org/mediawiki/2021/a/a6/T--OhioState--21PeerEditThumbnail.png)

#

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[Collaborations](https://2021.igem.org/Team%3AOhioState/Collaborations)

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[Description](https://2021.igem.org/Team%3AOhioState/Description)
[Notebook](https://2021.igem.org/Team%3AOhioState/Notebook)
[Graphic Design](https://2021.igem.org/Team%3AOhioState/GraphicDesign)

### **Wet Lab**

[Experiments](https://2021.igem.org/Team%3AOhioState/Experiments)
[Safety](https://2021.igem.org/Team%3AOhioState/Safety)
[Engineering](https://2021.igem.org/Team%3AOhioState/Engineering)
[Parts](https://2021.igem.org/Team%3AOhioState/Parts)

### **Dry Lab**

[Model](https://2021.igem.org/Team%3AOhioState/Model)

### **HP**

[Communication](https://2021.igem.org/Team%3AOhioState/Communication)
[Inclusivity](https://2021.igem.org/Team%3AOhioState/Inclusivity)
[Human Practices](https://2021.igem.org/Team%3AOhioState/Human_Practices)
[Regulations](https://2021.igem.org/Team%3AOhioState/Regulations)

### **Results**

[Results](https://2021.igem.org/Team%3AOhioState/Results)
[Contribution](https://2021.igem.org/Team%3AOhioState/Contribution)
[Implementation](https://2021.igem.org/Team%3AOhioState/Implementation)
[Proof of Concept](https://2021.igem.org/Team%3AOhioState/Proof_Of_Concept)

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# Team:OhioState/Engineering

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[Experiments](https://2021.igem.org/Team%3AOhioState/Experiments)
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Dry Lab▼

[Model](https://2021.igem.org/Team%3AOhioState/Model)

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[Communication](https://2021.igem.org/Team%3AOhioState/Communication)

[Human Practices](https://2021.igem.org/Team%3AOhioState/Human_Practices)
[Regulations](https://2021.igem.org/Team%3AOhioState/Regulations)

Results▼

[Results](https://2021.igem.org/Team%3AOhioState/Results)
[Contribution](https://2021.igem.org/Team%3AOhioState/Contribution)
[Implementation](https://2021.igem.org/Team%3AOhioState/Implementation)
[Proof of Concept](https://2021.igem.org/Team%3AOhioState/Proof_Of_Concept)

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[Description](https://2021.igem.org/Team%3AOhioState/Description)
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[Experiments](https://2021.igem.org/Team%3AOhioState/Experiments)
[Safety](https://2021.igem.org/Team%3AOhioState/Safety)
[Engineering](https://2021.igem.org/Team%3AOhioState/Engineering)
[Parts](https://2021.igem.org/Team%3AOhioState/Parts)

Dry Lab▼

[Model](https://2021.igem.org/Team%3AOhioState/Model)

Human Practices▼

[Communication](https://2021.igem.org/Team%3AOhioState/Communication)

[Human Practices](https://2021.igem.org/Team%3AOhioState/Human_Practices)
[Regulations](https://2021.igem.org/Team%3AOhioState/Regulations)

Results▼

[Results](https://2021.igem.org/Team%3AOhioState/Results)
[Contribution](https://2021.igem.org/Team%3AOhioState/Contribution)
[Implementation](https://2021.igem.org/Team%3AOhioState/Implementation)
[Proof of Concept](https://2021.igem.org/Team%3AOhioState/Proof_Of_Concept)

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#

# Engineering Success

## Dry Lab

### Kinetics Route

Once we had decided on our proteins, we wanted to be able to predict which one would work best, before testing them in the lab. The metric for “best” would be the protein that binds the strongest to lipid A, with a high rate. We conducted research to find kinetic data and rate constants for each modifier reacting with lipid A. This data was not available, so we returned to searching for ideas until we found docking.

### Docking

Using crystal structures, the docking method outputs docking scores for a protein binding to a substrate. Originally, this seemed perfect, our team had both proteins and a substrate, so we expected to be able to get a docking score. However, after talking with Austin Cool, it became much more complicated than that. Firstly, docking primarily uses Gibbs Free Energy calculations to determine the docking score, but lipid molecules (like lipid A) notoriously sidestep and disrupt these calculations due to their long carbon chains (leading to inaccurate results). Secondly, there is no method to know if the way the docking program orients and folds the proteins is accurate, since the proteins aren’t well documented binding to lipid A. Because of this, even if we got results, we would have no way to validate them and no way to do a rough check to see if the model results even make sense. We would just have to blindly accept them. Thirdly, with only seven proteins, it would be relatively easy to get experimental results and discover the best anti-lipid A protein to use. This would eliminate all the guesswork inherent to our model, effectively making the docking model not worthwhile.

### Mutation Model

It wasn’t until meeting with Dr. Strathdee and Dr. Patterson
that we turned our attention to a bacterial mutation model. Bacteria mutating away from the select phage is a critical issue with phage therapy. We thought we might try to create a model to give a rough estimate of the amount of time it would take for the bacteria to mutate in such a way that renders our lambda phage non-functional. After another round of reaching out to experts and consults, we had to alter this idea. The randomness of mutation made it hard to give a set time as to when the bacteria would mutate out, it could be hours or it could never happen at all, it was all random and equally likely.

### Bacteria-Phage Population Model

Our final model iteration came when we talked with Dr. Raman and adapted a bacteria mutation model to a bacteria-phage population model. Given initial parameters, we found steady state concentrations of both bacteria and phage. We initially used parameters unspecific to E coli. and Lambda. Once we had the process set, we changed these parameters to reflect both our specific bacteria and phage. In doing so, we had outputs closer to what the actual E coli. Lambda Populations would look like.

![Dry Lab Flowchart](https://static.igem.org/mediawiki/2021/6/6d/T--OhioState--EngineeringSuccessFlowChart2.png)

Figure 1. Dry Lab Path to Final Model

## Wet Lab

### Anti-lipid A molecules

Once our team researched and decided on our project, using phage therapy to target lipid A, the first part of the project we needed to solve was what molecule can we use to target lipid A. After in depth research, we compiled a list of over 20 different molecules that we thought could produce the results we wanted. These molecules varied widely in how they interacted with lipid A and what host species in which they were naturally found. We then took our list of molecules and met with multiple researchers who study sepsis, including Dr. Steve Abedon, Dr. John Gunn. Taking what we learned from them, we whittled our list down from 20 molecules to seven individual molecules and one pathway of molecules. In addition, we researched various assays that would actually indicate if we had lipid A in our final product. However, after consulting with these researchers, we dropped most of our tissue culture assays in favor of a HEK TLR4 reporter cell line.

### FraR

Another important detail in our project was our choice of promoter. The problem we had was that the genes in our phage are toxic to *E. Coli*, so we needed our genes products to not be present in the lab strain *E. Coli*, in which the phage grew, but present in wild type *E. Coli*. This problem was solved by implementing a FraR-pFraB system. The FraB gene is involved in fructose asparagine metabolism and is strongly repressed by the FraR protein when not in the presence of fructose asparagine. The *E. Coli* we would use to develop our phage would not express our anti-lipid A molecules, but wild type *E. Coli* lack this gene and would express it. Therefore, our system would be active as a promoter in wild type *E. Coli* and completely inactive in the lab strains.

![pFraB Graph](https://static.igem.org/mediawiki/2021/a/a6/T--OhioState--pFraB.jpg)

Figure 2. pFraB Luciferase Reporter

We designed the experiment initially to test whether our promoter would function correctly or not by creating reporter strains by cloning our promoter into a Lux reporter pSB401. Then we transformed into a strain that expressed fraR inducible by IPTG via a plasmid. We then attempted to transform into a strain that didn’t express fraR. However, upon initial testing we realized that strain that contained two plasmids with two different antibiotic resistances would grow slower and thus have lower luciferase levels. So we transformed the strains without the fraR plasmid with an empty vector containing the antibiotic in order to ‘burden’ both strains a similar amount. After doing this we observed that pFraB worked as expected with strong repression in the fraR(+) strains with IPTG.

However, we were wondering whether we could create a promoter with the same regulation as pFraB but a stronger expression. This was because we want our phage to cause the expression of these genes very fast and at a high level before the cells lyse from the immune response. So we decided to add the pR promoter(BBa\_R0051) upstream of the pFraB promoter(BBa\_K3783000) to create a combination promoter "pC" (BBa\_K3783001). So we could create a system that would highly express in our wild type E.coli but be repressed in our lab strain so we could grow our phage clones. When we went to test our promoter we saw a 30 fold increase in promoter strength. This promoter also maintained the regulatory properties of pFraB with strong repression from fraR. The protein fraR was induced by the addition of IPTG as seen previously in the fraR(+) strains.
![pR-pFraB Graph](https://static.igem.org/mediawiki/2021/1/1b/T--OhioState--pR-pFraB.jpg)

Figure 3. pR-pFraB Luciferase Reporter

#

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#

# Experiments

### Below you can find a downloadable pdf of our experiments and protocols from this year.

Download the file [here](https://static.igem.org/mediawiki/2021/a/ac/T--OhioState--21Experiments.pdf).

#

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#

# Results

## Promoters

For our contribution, we tested 2 individual promoters, pFraB (BBa\_K3783000) and pR (BBa\_R0051), and 1 fusion promoter, pR-pFraB (pC) (BBa\_K3783001). We coupled these promoters to a luciferase reporter plasmid pSB401 in fraR positive and fraR negative strains of *E. coli*. They were tested with and without IPTG due to its upregulation of fraR. All of the luminescence data was normalized to the OD across 20 hours.

### FraR

The fraR+ strains worked as intended, by repressing the expression of luciferase compared to the fraR- strains (their relative light units are the lowest in figure 1). The presence of IPTG should affect the luminescence because fraR is expressed by a lacZ promoter, however there was only a slight difference in the two trials.

![pFraB Graph](https://static.igem.org/mediawiki/2021/a/a6/T--OhioState--pFraB.jpg)

Figure 1. pFraB Strength Measured by Luminescence

### Controls

We ran two controls. The pR promoter by itself served as a positive control and provided more characterization for an existing part. An empty pSB401 vector, served as the negative control. The pR worked as expected; it had a strong expression of protein regardless of the presence of IPTG. Seen in figure 2, both of the pR series have high relative light units no matter if IPTG was present. The negative control also produced expected results with negligible luminescence produced. We submitted this luminescence data of pR to the biobrick registry in order to provide more characterization data of this promoter to future iGEM teams.

![Control Promoter Graph](https://static.igem.org/mediawiki/2021/5/54/T--OhioState--pR-Graph.jpg)

Figure 2. Control Promoter Strength Measured by Luminescence

### Fusion Promoter

For our fusion promoter, a noticeable decrease- about threefold- was observed between the fraR+ and fraR- strains, as expected. The presence and absence of IPTG seems to be trivial although its effects can be seen in figure 3 and are consistent. The fraR- strain with IPTG had the highest levels of expression, and although they should hypothetically have the same expression, fraR- with IPTG expresses slightly less. The fraR+ strains do show a significant decrease though, with the IPTG trial being slightly more repressed than the trial without IPTG. All of this follows our hypothesized function.

![pR-pFraB Graph](https://static.igem.org/mediawiki/2021/1/1b/T--OhioState--pR-pFraB.jpg)

Figure 3. Fusion Promoter (pR-pFraB) Strength Measured by Luminescence

### Effect of Fusion

Attention should be given to the y axis and the scale of the relative light units. The production in the fusion promoter (figure 3) is in the millions, whereas the other promoters characterized (figures 1 and 2) are in the hundreds of thousands, which is a tenfold difference. We first theorized that this could be due to the pR promoter overpowering the repression of pFraB, but the pR promoter by itself does not show such magnitude. We finally concluded that this overexpression could be due to the presence of two constitutive promoters stitched together, which could overpower fraR repression of pFraB.

## Proteins

### LAL Assay

Our plans deviated from original because of unexpected delays of a phage cloning kit due to the pandemic. Therefore, we were not able to test our system using phage to infect bacteria and then testing the product. Instead, we had to test our system in parts, one to determine promoter efficacy, which was our characterization, and one to determine the effects of the anti-lipid A proteins.

Due to time constraints, we only had the chance to test our constructs with one endotoxin detection kit- the LAL assay. We lysed our TOPO constructs and control, diluted, and compared to a control strain that had unmodified lipid A. We converted figure 4’s axis from quantitative absorbance data to Endotoxin Units, which were based on standards that the kit provided.

![LAL Assay Graph](https://static.igem.org/mediawiki/2021/b/b0/T--OhioState--LALAssayGraph.png)

Figure 4. Endotoxin Units of Construct Lysates via LAL Assay

Almost all of our anti-lipid A proteins decreased the amount of lipid A compared to the control, with the exception of pFraB-LF. It should be kept in mind that LF (lactoferrin) and LptA are the only two proteins whose mechanism of action is to physically bind lipid A (binding proteins). Therefore, it makes sense that pC, whose characterization data showed great levels of expression, would increase the efficacy of irreversible binding proteins. The other proteins, whose mechanism is modification of lipid A, are much less consistently affected by the pC promoter.

The one that appears to work the best is pFraB-LpxR, however, the full pathway, LpxEFR does not seem to be much more effective. This holds true for PagL/PagP and PagLP which is another pathway divided into parts. This implies that the effects of these anti-lipid A binding proteins are not necessarily additive.

#

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![ASC Logo](https://static.igem.org/mediawiki/2021/c/c2/T--OhioState--ASCLogo2021_1.svg)

![OOR Logo](https://static.igem.org/mediawiki/2021/6/60/T--OhioState--OORLogo2021.svg)

### **Home**

[Home](https://2021.igem.org/Team%3AOhioState)

### **Team**

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

### **Project**

[Description](https://2021.igem.org/Team%3AOhioState/Description)
[Notebook](https://2021.igem.org/Team%3AOhioState/Notebook)
[Graphic Design](https://2021.igem.org/Team%3AOhioState/GraphicDesign)

### **Wet Lab**

[Experiments](https://2021.igem.org/Team%3AOhioState/Experiments)
[Safety](https://2021.igem.org/Team%3AOhioState/Safety)
[Engineering](https://2021.igem.org/Team%3AOhioState/Engineering)
[Parts](https://2021.igem.org/Team%3AOhioState/Parts)

### **Dry Lab**

[Model](https://2021.igem.org/Team%3AOhioState/Model)

### **HP**

[Communication](https://2021.igem.org/Team%3AOhioState/Communication)
[Inclusivity](https://2021.igem.org/Team%3AOhioState/Inclusivity)
[Human Practices](https://2021.igem.org/Team%3AOhioState/Human_Practices)
[Regulations](https://2021.igem.org/Team%3AOhioState/Regulations)

### **Results**

[Results](https://2021.igem.org/Team%3AOhioState/Results)
[Contribution](https://2021.igem.org/Team%3AOhioState/Contribution)
[Implementation](https://2021.igem.org/Team%3AOhioState/Implementation)
[Proof of Concept](https://2021.igem.org/Team%3AOhioState/Proof_Of_Concept)