<p>In the design phase of our project, the input from stakeholders was instrumental. In an iterative process of design and feedback, we came to a final design incorporating 5 requirements that our biosensor would have to fulfill in order to be successful.</p>
<p>In the design phase of our project, the input from stakeholders was instrumental. In an iterative process of design and feedback, we came to a final design incorporating 5 requirements that our biosensor would have to fulfill in order to be successful.</p>
<h4>Biosensor Design: Establishing requirements in concert with experts and stakeholders </h4>
<h4>Biosensor Design: Establishing requirements in concert with experts and stakeholders </h4>
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<p>=<a href = "https://2018.igem.org/Team:Utrecht/Human_Practices" >Feedback sessions</a> with stakeholders were instrumental in the design of our biosensor. At the start, we knew that chemical contamination of surface water is a severe threat to this valuable resource. The sources of contamination can be unexpected, like the dumping of chemical waste from illegal drug production. However, early sessions with stakeholders taught us that the most commonly encountered contamination is from common pharmaceuticals, like antidepressants. We therefore focused our efforts in this direction. In an iterative process of design and feedback, we identified the following 5 points as key requirements for our biosensor: </p>
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<p><a href = "https://2018.igem.org/Team:Utrecht/Human_Practices" >Feedback sessions</a> with stakeholders were instrumental in the design of our biosensor. At the start, we knew that chemical contamination of surface water is a severe threat to this valuable resource. The sources of contamination can be unexpected, like the dumping of chemical waste from illegal drug production. However, early sessions with stakeholders taught us that the most commonly encountered contamination is from common pharmaceuticals, like antidepressants. We therefore focused our efforts in this direction. In an iterative process of design and feedback, we identified the following 5 points as key requirements for our biosensor: </p>
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<li>accurate detection at different concentrations;</li>
<li>accurate detection at different concentrations;</li>
Our project started with the observation that chemical contamination of surface water is an increasingly important problem. We felt that a biobased sensor would offer excellent opportunities for detection of such contaminants, and identified the bacterial chemotaxis pathway as the best point of entry to engineer a flexible and sensitive sensor. Below, we describe the design and implementation of our project.
Overview
The goal of our project is to engineer the chemotaxis pathway of the bacterium Escherichia coli to emit a visible light signal that lets us know whether specific contaminants are present in water, and at what levels.
Design
In the design phase of our project, the input from stakeholders was instrumental. In an iterative process of design and feedback, we came to a final design incorporating 5 requirements that our biosensor would have to fulfill in order to be successful.
Biosensor Design: Establishing requirements in concert with experts and stakeholders
Feedback sessions with stakeholders were instrumental in the design of our biosensor. At the start, we knew that chemical contamination of surface water is a severe threat to this valuable resource. The sources of contamination can be unexpected, like the dumping of chemical waste from illegal drug production. However, early sessions with stakeholders taught us that the most commonly encountered contamination is from common pharmaceuticals, like antidepressants. We therefore focused our efforts in this direction. In an iterative process of design and feedback, we identified the following 5 points as key requirements for our biosensor:
accurate detection at different concentrations;
clear and easily measurable detection signal;
possibility for detecting diverse ligands;
rapid signaling;
low-cost system.
Chemotaxis: the optimal system to meet all requirements
We chose to base our biosensor on the bacterial chemotaxis system because it offers many opportunities to address the above considerations. Because it is a native bacterial system, it can be produced at low cost (requirement V) and responds rapidly to environmental input (requirement IV).
In their natural environment, the chemotaxis system is used by Escherichia coli bacteria to direct them towards the highest concentration of a ligand. This system consists of a receptor that can be modified to detect a wealth of compounds (requirement III), (Shuangyu Bi et al. 2016). Furthermore, this receptor can be methylated, causing the sensitivity of the receptor to decrease. Customizing the methylation states facilitates the ability to measure a set concentration range (requirement II).
When no ligand is bound to the chemotaxis receptor, it activates a signal transduction pathway of several proteins, including CheA, CheY, and CheZ (figure 1). First, CheA phosphorylates CheY, which subsequently translocates to the cell membrane where it binds the flagellar motor protein, thereby altering its rotational direction to a “running” state. After CheY is bound to the flagellar motor protein, it is subsequently dephosphorylated by CheZ. Upon ligand binding, the receptor is inactivated, leading to an inactivated pathway and a rotational direction of the flagellar motor protein in the “tumbling” state. Since the binding of CheY and CheZ is linked to ligand binding of the receptor, measurement of the binding of these proteins by using the BRET assay (described below) provides a quick and accurate indication of the concentration of ligand present (requirement I and II).
Chemotaxis pathway modifications to address all considerations
To create a biosensor meeting all five listed requirements, the E. coli chemotaxis pathway was customized using a three step approach. First, we set up an assay to verify the extent to which one of the most important chemotaxis receptors, the Tar receptor, could be customized. This enables accurate detection and the possibility to detect diverse ligands (requirement III). Next, a method establishing the ability to measure the activity of this pathway was implemented, using a Bioluminescence Resonance Energy Transfer (BRET)-pair. BRET provides a clear and easily measurable detection signal (requirement II and IV). The last step of the approach facilitates accurate detection at different concentrations, by mimicking receptor methylation. This facilitates detection of different ligand concentrations (requirement I).
Bioluminescence Resonance Energy Transfer Pairs
To measure the concentration of ligand present, the activity of wild type and modified Tar receptors have to be measured. Therefore, we designed a Bioluminescence Resonance Energy Transfer (BRET) based sensor, inspired by a previously made chemotaxis BRET-pair (Cui et al. 2014). BRET is a technique where photons produced by a luciferase are used to excite a fluorophore. We opted to use Renilla luciferase (RLuc) since it produces photons that can excite eYFP. In addition, the substrate for this protein, coelenterazine, is permeable to the cell which makes it perfect for E. coli based bioassays. For energy transfer to occur, the RLuc and eYFP proteins must be less than 10 nm apart. Thus by measuring the ratio of light emitted by the two molecules, we can detect whether they are physically close, as can be seen in figure 2.
By fusing the RLuc to CheZ and eYFP to CheY, the bioluminescence can be used as a proxy to demonstrate whether the chemotaxis proteins interact (Figure 3). When no ligand is bound to the Tar receptor, CheA is active and phosphorylates CheY. Because of this, the phosphorylated eYFP::CheY can interact with CheZ::Rluc, bringing RLuc and eYFP in close proximity of each other. This can be measured as a BRET signal when the RLuc substrate is added. Upon binding of a ligand to the Tar receptor, CheA becomes inactivated, ultimately leading to a decrease of the BRET signal. Importantly, the signal can be readily measured by using an affordable bioluminescence assay.
Figure 1: The Chemotaxis Pathway of E. coli. A) The active pathway. B) The inactive pathway.
Figure 3: The implementation of the BRET-pair in the Chemotaxis pathway. A) the BRET-pair is able to get in close proximity when no ligand is bound to the receptor. B) Upon binding of ligand to the receptor, the pathway is inactivated. CheY and CheZ no longer bind, therefore YFP will not be excited by Luciferase.
Figure 2: BRET-pair activity. A) When CheZ and CheY do not interact, the distance between Luciferase and eYFP is too large for Luciferase to excite eYFP. As a consequence, only Luciferase emits light. B) Upon interaction of CheZ and CheY, Luciferase and eYFP are in close proximity. eYFP is excited by photons produced by luciferase, leading to luminescence of eYFP.
Receptor Assay
The receptor assay was designed as primary control experiment to check the functionality of the transmembrane fusion receptor. We fused the Tar ligand binding domain (LBD) to the two-component copper sensitive (CusS) receptor. This new hybrid receptor protein detects aspartate instead of copper, and serves as a proof of concept for the ability to create hybrid receptors by exchange of LBDs from two different receptors. The functionality of the fusion protein was verified by using the downstream Cuss pathway.
When a ligand specific for the LBD is bound the receptor, a signal is transduced to the response regulator protein (CusR) which binds to and activates a copper sensitive promoter. Normally, the CusR promoter activates genes involved in copper resistance protecting the bacterium against this potentially harmful transition metal.
In this experiment, the promoter region was coupled to a red fluorescent protein (RFP) sequence. Thus, when aspartate is bound to the receptor, CusR is activated as well as the downstream promoter and RFP is transcribed. This way, the expression of RFP serves as an indicator for a functional Tar-CusS receptor. RFP was used as indicator as it is easily measured using fluorescent light microscopy. The custom receptor was ordered at Integrated DNA Technologies (IDT) while the CusR promotor and RFP were transformed from the iGEM biobrick kit.
Methylation of the Tar receptor
Figure 4: Chemotaxis-coupled BRET activity. (A) Represents a situation in which no ligand is present and where the chemotaxis pathway is active. In this case phosphorylated CheY and CheZ interact, inducing a BRET associated luminescence. (B) Represents a situation with a high concentration ligand present, leading to an inactivated pathway. BRET associated luminescence is eliminated. (C) The same concentration of ligand is present as in B. However, since the receptors are methylated, more ligand has to be added in order for the pathway to be inactivated. This results in less YFP luminescence and relatively more luciferase luminescence, resulting in light tending more to blue then to green
Broader concentration measurements by methylation mimicking
Jos van der Vosse (TNO) suggested quantifiable measurements. Since we did not know the sensitivity of our light signal system at that time, we decided to implement an additional method, facilitating the measurement of set ranges of concentrations. This method is based on the methylation state of the Tar receptor, which influences its sensitivity to ligands.
In the unmethylated state, the receptor transduces the ligand binding signal to an inactivated pathway (Figure 4A and 4B). Upon receptor methylation, the conformational change required to transduce the inactivation signal, is energetically less favorable, resulting in lower occurrence. Therefore, when the same amount of ligand is present, the level of pathway inactivation is lower than in the unmethylated receptor state. Since the Tar receptor has four acidic residues that can be methylated (Q295, E302, Q309, E491), there are in total sixteen different combinations of methylation states, each one resulting in a slightly different sensitivity (Figure 4C).
Application of methylation in our biosensor
The methylation of glutamine (Q) or glutamic acid (E) residues can be mimicked by mutating these residues to alanine (A) (Krembel et al. 2015). The different possible combinations of methylation mimicking alanine mutations yields a wide range of potential sensitivities, which we predicted with our computational ODE-model. As a proof of concept, we chose three different methylation states:
The wild type state, the unmethylated Tar receptor (QEQE, most sensitive);
A Tar receptor with one mimicked methylation site (QEAE, average sensitivity);
A Tar receptor with two mimicked methylation sites (QEAA, least sensitive).
The Tar receptor that we received from a collaboration with iGEM team Groningen was mutated using site-directed mutagenesis and expressed in E. coli strains UU1250 and VS181, which do not express any chemotaxis receptors. In addition, VS181 does not express the methyltransferases CheW and CheB, resulting in a fixed methylation state. The E. coli strains were kindly provided by Shuangyu Bi from the lab of V. Sourjik at the Max Planck Institute for Terrestrial Microbiology in Marburg.
Next, the sensitivity of E. coli for aspartate could be measured. This was achieved by addition of a range of aspartate concentrations and subsequent luminescence measurements by using the self-designed BRET-pair. Our model can be verified and corrected based on these measurements. BRET measurements were validated using a FRET-pair received from Marburg.
Cui B, Wang Y, Song Y, et al. (2014) Bioluminescence Resonance Energy Transfer System for Measuring Dynamic Protein-Protein Interactions in Bacteria. mBio. 5(3):e01050-14. DOI:10.1128/mBio.01050-14.
Krembel A, Colin R, Sourjik V (2015) Importance of Multiple Methylation Sites in Escherichia coli Chemotaxis. PLoS ONE 10(12): e0145582.
Shuangyu Bi, Abiola M. Pollard, et al. (2016) Engineering Hybrid Chemotaxis Receptors in Bacteria. ACS Synthetic Biology 5(9), 989-1001. DOI: 10.1021/acssynbio.6b00053
