Introduction

Our initial design was quite ambitious. We embarked on creating a project comprised of multiple components, each of which turned out to be more complex than we had initially anticipated. Despite the constraints of the timeframe and limited resources, we managed to address some questions and make necessary updates to our designs. However, there are elements we encountered as potential improvements and there remain questions unanswered that could significantly refine our product.

We firmly believe that our project concept holds great potential for the medical industry, provided it undergoes further development. Additionally, we see great potential in our theorized bricks as a solid foundation for future teams to build upon, acknowledging the importance of advancing our project as an integral part of the iGEM community. With this in mind, we have chosen to delve into several key discussion points that we believe are crucial for consideration in the event of further project development.

Bacteria

One of the main points of discussion that we have encountered is the bacteria of the biosensor. The main issues are related to the presence of our biosensor bacteria in the human body. 

The bacteria need to be contained in some way that allows analytes to reach it while preventing the bacteria from dispersing in the body. One option we explored was encapsulating or immobilizing the cells in a porous polymer with pores large enough to allow bacteriophages and signaling molecules to go through, but not the E. coli.

At the same time, the biosensor needs to be placed in such a way that the light sensor is able to detect the signal it emits. There should be enough bacteria present such that a strong enough signal is produced. For this, some modeling could be done where the sensitivity threshold of the device is linked to the amount of bacteria. Further experiments should be carried out to determine what the function of light produced by a certain amount of bacteria is. Then, the total population of our biosensor required to pass the detection threshold of the device can be calculated.  

To overcome potential problems with passing the detection threshold, the cells could be immobilized at the end of the optic fiber cable used to detect the signal. Additionally a shorter cable length should also decrease the detection threshold, requiring less bacteria to be present.

Another important concern is whether the bacteria can actually survive the human body. Depending on the position of the biosensor, the bacteria will either be exposed to the blood, the interstitial fluid, or the synovial fluid around the joint. More research needs to be done to compare the different amounts of nutrients, metabolites and oxygen present in these fluids compared to the required amounts for cell survival. 

Perhaps the most important point regarding the bacterial cells is their biocontainment. A detailed biocontainment approach can be found in the [Safety page]. The method we thought of was physical containment, but leakage can occur. In the case of leakage, a different killswitch should be implemented. One idea would be adding specific antigens on the surface of the cell that could be recognized by the human antibodies and subsequently eliminated. CRISPR-based killswitches have also been recently reported for applications in gut bacterial treatments, using chemical or temperature induction [1]. 

Bacteria also tend to be present in two forms, either as planktonic or in a biofilm, which means that the biosensor bacteria is also capable of forming a biofilm. It is still unclear what induces bacteria to go into biofilm mode, but it involves several environmental and transcriptional factors [2-3]. Bacteria actually are mostly present in nature in biofilm form, as it increases the colony's chances of survival. Through differentiation, bacterial cells in a biofilm can assume different roles in order to divide labor amongst themselves [4]. 

If we want our biosensor bacteria to survive long-term, perhaps having them in biofilm form might actually be more beneficial, providing there is a way to avoid them escaping the physical containment. The downside would be reduced metabolism and dispersion of inducers amongst the population. Lab experiments can be conducted where different sizes of biosensor biofilm are exposed to expected concentrations of inducer and measuring their fluorescent output. A balance can be found so that the population produces a signal that is above the detection threshold.

However, we want to avoid pathogens forming biofilms on the outside of the enclosure. This makes the choice of material more difficult, as it should promote biofilm formation inside the enclosure but not outside. 

One idea we had that would solve most of these issues is having the bacteria physically contained outside of the body and administering purified phage+Dispersin B constructs upon the signal of an infection. Subsequently, a non-living biosensor strategy could be implemented that senses biofilm formation and induces phage production in the bacteria. Researchers have found non-invasive as well as constant monitoring systems for biofilm sensing that do not include live bacteria [12-13].

Fluorescent Light

There are also several concerns around using fluorescent light as a detection method in general. First of all, in order to fluoresce, the bacteria need to be excited first. Being able to both excite and read the signal with the same device, while keeping its size small enough to be wearable is a major challenge. The same optic fiber cable can be used both for excitation and detection, however light filters are required in order to ensure that the excitation light is not transmitted back to the detector. There also needs to be a light source that emits the excitation light, which would further complicate the design. Secondly, as previously mentioned, the amount of light produced needs to be able to be detected by the sensor inside the body, which may also prove to be a challenge.

Another helpful comment we received is about the detection area we aim to cover. Considering, for example, a hip implant has a very large surface area. How can we be sure that there is no biofilm forming anywhere else on the implant outside of the small detection area covered by the sensor? Biofilm can develop anywhere on the prosthetic, so the detection method for a potential biosensor should also take into account being able to detect more global factors that signal infection.

Promoter

One thing that needs to be accounted for is the amount of cyclic-di-GMP that is expected to be present in the area around the biofilm. In order to model the behavior of our system inside the body, we would need information regarding the diffusion coefficient of cyclic-di-GMP in blood or the fluid where the device would be present, as well as the expected concentrations in and around the biofilm. However, we had a very difficult time retrieving such information, as it is not easy to quantify these parameters. There is not a lot of literature available about cyclic-di-GMP levels in or around cells, as this molecule is quite difficult to detect with conventional lab methods.

One major factor we did not take into account when designing the project is that the biosensor cells also produce cyclic-di-GMP endogenously. The promoter we decided to work with was designed by the CUG China 2022 iGEM team and is able to measure different concentrations of cyclic-di-GMP [10]. What we did not realize, however, was that in their measurements they detected endogenous levels of cyclic-di-GMP and then used a hydrolase to reduce the amount for the subsequent measurements. In this case, any other amounts of cyclic-di-GMP that we would like to measure would be considerably lower than the endogenous amounts already present in the cells, not making a difference in the expression. We had this realization while designing the constructs and experiments, which was too late for us to change the promoter. What we decided to do is to also introduce the hydrolase into our design so that we could reduce endogenous levels of cyclic-di-GMP as much as possible. We also explored the option of knocking out genes responsible for cyclic-di-GMP production in our cells, but we soon realized that the expression system for this signaling molecule is more complex and cannot be easily suppressed [9].

We also struggled to express and test the promoter in our lab, which further made us wonder if it would perhaps be a better idea to switch to a different promoter that is activated by a different signaling molecule. We also toyed with the idea of making the promoter modular by changing the repressor protein that cyclic-di-GMP binds, also referred to as the sensor module, in order to promote expression. In theory, modularity could be introduced by devising a well-working method for obtaining proteins or protein complexes that bind specific analytes, or by providing a library of several repressor proteins/protein systems for specific targets.

Modularity

When designing the biosensor we often came back to the idea of having a modular system that could easily accommodate different sensing needs. In order to achieve that, different parts of the system should be interchangeable. For example, one way to make the sensor modular is to introduce a riboswitch-based promoter. 

Riboswitches are RNA fragments that fold onto themselves in different conformations, depending if there is a binding molecule present or not [5]. In this way they can exhibit switch-like behavior and can be used as promoters, one conformation blocks the transcription of a gene while another allows the gene to be read out by exposing the ribosome binding cassette [5]. Riboswitches could be used in the context of the biosensor as the sensing method, by selectively binding certain analytes and thus inducing the expression of a readout gene. Suppose a large enough library of riboswitches is created, then different analytes can be used as a detection method. 

One other place where modularity can be introduced is on the cargo that the bacteriophages carry. It is well known that phages can be used for peptide and (some) protein display as a method for selecting good binders [11]. There is versatility in what can be displayed on the outside of the phages. In our case, we decided to use a biofilm-degrading protein, dispersin B, but depending on the target of the sensing, other peptides or proteins can be attached.

Phages

Some questions we ran into while researching phage therapy and phage applications in biofilm treatment were also related to the specificity of the phages. We initially decided to use specific phages for E. coli, since that would be easiest to test in the lab. However, we received feedback that it might actually be a better idea to use non-specific phages since oftentimes biofilms are not composed of solely one species of bacteria. A cocktail of different phages can also be released as a temporary treatment, however, that would require intensive cloning efforts. 

Phages can be administered via various routes [6]. Besides our biosensor designing, two routes that we thought also had potential in the context of orthopedic infections are intramuscular injections. Perhaps oral administration upon biofilm detection would reduce the complexity of the design and be less invasive. However, the phage construct would need to be able to effectively pass through the digestive tract and an M13 phage in combination with Dispersin B could negatively impact the biofilms in the microbiome. Therefore intramuscular injections may be the most realistic alternative.

New Device

So what is next? Looking at the state of the art in terms of wearable biosensors, there are a lot of possibilities that could be incorporated into our design. We received feedback about how cables are not really a good option for sensing around prosthetic implants, as these are usually highly mobile areas. At the same time, having external cables in direct contact or around the implant can actually increase the chances of the implant developing infections or inflammation. So it is clear that a wireless device with the least invasiveness possible is desired. 

Currently, continuous glucose monitors are among the only commercially available wearable sensors that detect analytes instead of physical factors such as temperature and ph. Continuous glucose monitors use immobilized redox enzymes that oxidize glucose from blood and report on the measured amount. Enzymes are very well suited for continuous monitoring since they can regenerate independently. However, their stability and activity often is lower when they are immobilized. We also looked into the possibility of using redox enzymes for our sensor but we were unsure what redox active compound to use as a target.

Another new method that has been reported recently uses something called molecularly imprinted polymers (MIPs), in order to sense both redox and non-redox active molecules. These MIPs act as artificial antibodies that can recognize virtually any analyte. More information about this is available here [7]. 

Another new development in the area of biosensors is a non-invasive sweat-based biosensor that is able to detect an inflammation-associated molecule, C-reactive protein [7]. Although this sensor is not specific for biofilm-associated inflammation, it would be a viable and non-invasive option for patients who have undergone surgery [7]. 

Commercial Implementation

Producing a product for medical use requires stringent testing procedures and safety guarantees, for good reason. In the European Union, for a medicinal product to be available for market use, the European Medicine Agency (EMA) has to approve the application [8]. Especially with our set-up that requires a live and metabolizing bacterial host, one can assume the difficulty of getting such a system approved for medical use. Additionally, there are many more advanced options that are less risky and less invasive. However, with the problem of antibiotic resistance of biofilms in particular, we believe that the system of Dispersin B phage display has potential when it comes to medical implementation.

References

[1] Rottinghaus, A.G., Ferreiro, A., Fishbein, S.R.S. et al. Genetically stable CRISPR-based kill switches for engineered microbes. Nat Commun 13, 672 (2022). https://doi-org.proxy-ub.rug.nl/10.1038/s41467-022-28163-5

[2] Karatan, E., & Watnick, P. (2009). Signals, regulatory networks, and materials that build and break bacterial biofilms. Microbiology and molecular biology reviews : MMBR, 73(2), 310–347. https://doi.org/10.1128/MMBR.00041-08

[3] An, D., & Parsek, M. R. (2007). The promise and peril of transcriptional profiling in biofilm communities. Current Opinion in Microbiology, 10(3), 292–296. https://doi.org/10.1016/j.mib.2007.05.011

[4] Momeni, B. (2018). Division of Labor: How Microbes Split Their Responsibility. Current Biology, 28(12), R697–R699. https://doi.org/10.1016/j.cub.2018.05.024

[5] Garst, A. D., Edwards, A. L., & Batey, R. T. (2011). Riboswitches: structures and mechanisms. Cold Spring Harbor perspectives in biology, 3(6), a003533. https://doi.org/10.1101/cshperspect.a003533

[6] Tay, A. (2023). How building a phage directory can mean life or death for patients. Nature, d41586-023-02209–0. https://doi.org/10.1038/d41586-023-02209-0

[7] Wang, M., Yang, Y., Min, J., Song, Y., Tu, J., Mukasa, D., Ye, C., Xu, C., Heflin, N., McCune, J. S., Hsiai, T. K., Li, Z., & Gao, W. (2022). A wearable electrochemical biosensor for the monitoring of metabolites and nutrients. Nature Biomedical Engineering, 6(11), 1225–1235. https://doi.org/10.1038/s41551-022-00916-z

[7] Velasco, E. (2023, June 22). Wearable Sweat Sensor Detects Molecular Hallmark of Inflammation. Caltech (California Institute of Technology). https://www.caltech.edu/about/news/wearable-sweat-sensor-detects-molecular-hallmark-of-inflammation

[8] About us: What we do. (n.d.). European Medicines Agency. https://www.ema.europa.eu/en/about-us/what-we-do

[9] Reinders, A., Sellner, B., Fadel, F., van Berkum, M., Kaczmarczyk, A., Ozaki, S., Rueher, J., Manfredi, P., Sangermani, M., Harms, A., Perez, C., Schirmer, T., & Jenal, U. (2021). Digital control of c-di-GMP in E. coli balances population-wide developmental transitions and phage sensitivity [Preprint]. Microbiology. https://doi.org/10.1101/2021.10.01.462762

[10] Description. (2022). CUG-Cina IGEM 2022. https://2022.igem.wiki/cug-china/description

[11] Bazan, J., Całkosiński, I., & Gamian, A. (2012). Phage display--a powerful technique for immunotherapy: 1. Introduction and potential of therapeutic applications. Human vaccines & immunotherapeutics, 8(12), 1817–1828. https://doi.org/10.4161/hv.21703

[12] Xu, W., & Ceylan Koydemir, H. (2022). Non-invasive biomedical sensors for early detection and monitoring of bacterial biofilm growth at the point of care. Lab on a chip, 22(24), 4758–4773. https://doi.org/10.1039/d2lc00776b

[13] Kurmoo, Y., , Hook, A. L., , Harvey, D., , Dubern, J. F., , Williams, P., , Morgan, S. P., , Korposh, S., , & Alexander, M. R., (2020). Real time monitoring of biofilm formation on coated medical devices for the reduction and interception of bacterial infections. Biomaterials science, 8(5), 1464–1477. https://doi.org/10.1039/c9bm00875f

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