Project Description

Abstract

Synthetic biology offers many potential cost-effective healthcare technologies. One of those could be new ways to diagnose and treat disease at an early stage. Current techniques for biomarker detection (e.g. ELISA, RIA) are time consuming, expensive and require specialised equipment.

We intend to design a microbiological system for the detection of low quantities of biomarkers. This assay aims to be easier and more cost efficient than existing techniques and possible to perform in modestly equipped settings. Initially, we will focus on the expression of a receptor for the desired biomarker. Depending on the nature of the biomarker, the receptor will be either be native or designed.

Upon biomarker detection, signal amplification will be triggered by our receptor system to activate a read out/detection system (e.g. Luciferase, GFP) inside the microorganism to artificially amplify the extracellular signal. In the next stage, the team will go on to design a read-out system to measure the concentration of biomarkers in body samples. Finally we want to investigate if we can make this system transferable to other biomarkers, changing only the receptor part of the system.

The system would be cheap, fast and possible to distribute without advanced equipment. Our motivation is to improve patient prognosis and quality of life and to improve efficiency and reduce costs within the healthcare system.

Modelling

The main aim of the modelling team is to design and optimize the signalling pathways regarding EnvZ-OmpR interactions, phosphorylated OmpR transcription, quorum sensing and fluorescent read-out, to further on investigate the magnitude of amplification given the concentration of the compounds taking part in the whole process. Based on previous research publications, we examine metabolites of our signalling pathway, their initial concentrations in bacteria and differential equations describing the kintetics of the reactions in our model. Using SimBiology, a programme that belongs to the Mathlab software, we try to establish our model by designing the reactions in our compartments , adjusting the rate constants and establishing reversible and irreversible reactions. We divide our model based on cell density and osmolarity in the extracellular environment to provide a better prognosis for the changing input parameters in the bacteria.

Recognition

We are building bacteria that will change color when they find these disease related proteins. Replacing only a small part of the system should allow us to detect thousands of different targets. To recognise these disease-related proteins we are building a chimeric membrane receptor based on EnvZ, a two component receptor native to E. coli.

To make the receptor recognise disease markers, we use small molecules known as Affibodies, originally developed at KTH Royal Institute of Technology, which can be engineered to bind to thousands of proteins. We are attempting to fuse this affibody to the periplasmic domain of EnvZ in such a way that binding will activate signalling in the cytoplasm. Initially we are testing this for an affibody which binds HER2, a protein shown to play a role in aggressive breast cancer. If we prove our concept to detect one target the conserved structure of Affibody molecules should mean that many other targets could be detected as well.

This is how our receptor works. A protein biomarker for disease binds to an Affibody molecule, which is fused to the periplasmic part of the EnvZ receptor. EnvZ is two component system, which activate kinases in the cytoplasm upon interactions with the periplasmic domain. There is evidence for that the transmembrane helix of EnvZ can be tweaked to respond to the conformational in the periplasmic region and activate histidine kinase, just as it does in normal EnvZ. When histidine kinase is activated, it turns on the regulatory protein OmpR which starts production of a signal molecule.

Since we are building on work that has been done on the EnvZ/OmpR system in the past, we are using E. coli as a host organism to reduce the number of unknown variables. To get large HER2 protein to our receptor in the periplasm, we have to degrade the cell wall and outer membrane. This is a delicate process, and future iterations of our proposed assay should attempt to express the system in gram positive bacteria to circumvent this problem.

Signalling

The objective of signal transduction in ABBBA is to receive a signal from the activated Bacterial Affibody Receptor and to deliver it to the Read-Out Strain. This involves converting an intracellular signal into an extracellular signal.

To achieve signal transduction we will construct a continuously controlled signaling system which produces a graded quorum sensing out-put. When the ratio of phosphorylated to dephosphorylated OmpR is high, expression of RhlI is increased while expression of LuxI is decreased. Conversely, when OmpR is dephosphorylated LuxI levels are increased and RhlI levels are decreased. LuxI and RhlI produce quorum sensing molecules OHHL and BHL respectively. They can diffuse freely across the cell membrane creating an extracellular signal that can be received by the Read-Out Strain.

Many signaling systems function as an on-off switch whereas the ABBBA regulatory circuit functions more like a dimmer - gradually adjusting the output in response to the input. This makes the system sensitive to both kinase and phosphatase activity in BAR. It also allows for quantification of biomarker levels.

Read out

The read-out of the system consists of a yes/no mechanism which operates using two different fluorescent proteins in a separate bacterial strain. In the case of no biomarker being bound to the receptor, the recognition/signaling strain will output one quorum sensing molecule (OHHL, negative) and with the biomarker bound it will output a different one (BHL, positive). Quorum sensing is communication between bacteria using so called auto-inducers. In our case we use two different homoserine lactones found endogenously in gram negative bacteria other then E. coli. The read-out strain will detect these two quorum sensing molecules separately and output a red fluorescent protein (mCherry) for the positive quorum sensing signal, and a green or yellow fluorescent protein (not yet decided which) for the negative quorum sensing signal. Over time it will hopefully be possible to quantify the amount of biomarker roughly as a higher concentration of biomarker will lead to a stronger red signal and a weaker green/yellow signal.

References

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