Team:UCopenhagen/Poster

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Welcome to the UCopenhagen Poster Page!

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iGEM UCopenhagen brings you our 2020 project: CIDosis
Presented by Team UCopenhagen 2020

Authors: Aje Al-Awssi, David Nørgaard Essenbæk, Emil Funk Vangsgaard, Endre Lindhardt Garberg, Ignacio Pardo Casado, Jan Weicher, Shivani Pradeep Karnik, Victoria Thusgaard Ruhoff & Vit Zemanek

Abstract
Chronic Inflammatory Diseases (CIDs) are debilitating diseases affecting millions of people worldwide. Optimal treatment requires constant monitoring, but current testing methods are invasive, time-consuming, and costly. CIDosis strives to change this with a non-invasive patch for self-monitoring. Backed by extensive computer modeling, we are developing a biosensor that continuously collects sweat from the skin, and produces a color reflecting the level of inflammation. The biosensor in our patch is based on Saccharomyces cerevisiae cells equipped with interleukin-specific receptors that will associate in the presence of interleukins, resulting in the intracellular complementation of a split protein. A transduction pathway is then triggered, leading to the production of a color, whose intensity is logged by an app and shared with a medical professional. By integrating the wishes of patients living with CIDs, as well as experts within these fields, CIDosis brings a next generation tool for patient empowerment.

Biosensor Design
Three interleukin biosensors of varying complexity have been developed. The biosensors all build on the principle of association, where the association of the extracellular human interleukin receptor domains leads to the intracellular association of two halves of a split protein. The designs utilize;

  • Split ubiquitin, the assembly of which leads to the release of a transcription factor (TF).
  • A split TEV protease, that cuts a membrane-bound TF loose.
  • Another split TEV protease, which will cut an engineered G-alpha (GPA1) protein into smaller fragments, allowing for beta-/gamma dissociation and subsequent signaling.
Modeling
Three distinct designs of engineered signaling pathways in S. cerevisiae were compared through modeling with ordinary differential equations (ODEs). The effects of hypothetical cellular scenarios on the pathways were modeled within the framework of stochastic differential equations (SDEs). The impact of mutations and cleavage upon protein binding was modeled with several generations of simulations using the Rosetta software.

The ODE models revealed the importance of a pheromone cascade in signal amplification (~7 orders of magnitude), thus rendering one of the designs as a clear candidate for the application in the biosensor. Further, various expected and unexpected behaviors were explored in the SDE models, suggesting that specific failures of pathway components may lead to characteristic statistics of reporter concentrations. Simulations of protein-protein interactions aided the selection of cleavage site locations in mutant proteins. Predictions showed an increase in affinity of the mutant proteins after cleavage, which formed a basis of a refined framework for engineering proteins required for high sensitivity of the biosensor.

Performance and behaviors of signaling pathways, and affinities of mutant proteins were modeled.
Biosensor Design
Three interleukin biosensors of varying complexity have been developed. The biosensors all build on the principle of association, where the association of the extracellular human interleukin receptor domains leads to the intracellular association of two halves of a split protein. The designs utilize;

  • Split ubiquitin, the assembly of which leads to the release of a transcription factor (TF).
  • A split TEV protease, that cuts a membrane-bound TF loose.
  • Another split TEV protease, which will cut an engineered G-alpha (GPA1) protein into smaller fragments, allowing for beta-/gamma dissociation and subsequent signaling.
Materializing the Product
Our solution is a sweat-collecting patch that the patient can wear on the go. It consists of three layers.
  1. Porous nanofilm - A porous nanofilm will allow interleukins to diffuse into the patch and prevent the yeast cells from escaping the patch. This film provides safety for the users and bio-containment.
  2. Genetically modified yeast-based biosensor - Our yeast biosensor in dry-yeast form, ready to be activated upon contact with sweat.
  3. Adhesive patch - Common transparent plastic or woven fabric (such as nylon) used by bandage manufacturers.
General inflammation can greatly fluctuate, and infrequent testing can give a misleading picture of a patient's inflammation status, due to the snapshot nature of these tests. For example, rheumatoid arthritis patients can experience big inflammation changes between current testing, leading to irrevocable damage. We want to avoid this by giving patients easy access to weekly stress-free monitoring. The CIDosis patch can be used in the patient’s daily life and help guide treatment by providing more data to the healthcare professionals. We envision the use of an app that would enable the users to read the inflammation results in a precise manner. This app will track the inflammation results over time. In this way, inflammation levels can be saved and used for disease progression analysis. Here you see a representation of our app with a color slider that allows the user to save the patch color to their calendar and follow their inflammation.
Section 3
Plasmids with desired constructs were made through USER cloning- and ligation in E. coli. Stable transformation of the yeast with purified plasmids from the USER step happened via use of a 5-assembly system, which allows for controllable expression of our gene of interest. Confirmation of positive yeast transformations was done through colony PCR. The biosensor designs were tested in interleukin induction assays, measured in luminescent units, as luciferase is expressed upon interleukin binding. GPA1 mutants were tested in the context of an adenosine biosensor employing the human adenosine GPCR receptor A2A(R199A). Subcellular localization assays of proteins of interest coupled to superfolding GFP were made with confocal laser scanning microscopy.
Biosensor things
What inspired your team? What motivated you to work on this particular project?
Human Practices
Human practice was instrumental in developing CIDosis. We talked with 10 experts and got 86 patient inputs from interviews and surveys. This not only affected the formulation of our, but also directed our lab efforts towards developing a modular design that can be used by other scientists. Furthermore, patient surveys helped us design the patch. Patient feedback gave us some indication of what the patch should look like, and it made clear the need for proper instructions for the user. This culminated in a user-guide that specifies how to use the patch, and how patients can log and use the results they receive.

Patient surveys also influenced our dry lab work. Feedback suggested that most CIDosis users would be willing to wear the patch for up to 24 hours. Since time does not seem to be an issue for most patients, we could model the optimal time period for wearing the patch. Our results show that after being worn for 5 hours, the patch is best able to distinguish between high and low inflammation.

Finally, Throughout the project we worked on the ethical aspects CIDosis. These considerations spanned from potentially causing mental distress to patients, to the dangers of GMO products. Expert input was invaluable in resolving these issues, but we also collaborated with other iGEM teams who faced similar issues. The experience gained from working on ethical issues culminated in an ethics guide designed to make the process easier for future iGEM teams.
Biosensor things
What inspired your team? What motivated you to work on this particular project?
Idea
How are you going to solve the problem? Where did the idea come from?
References and Acknowledgements
If not already cited in other sections of your poster, what literature sources did you reference on this poster? Who helped or advised you?