Quorum Sensing

For the final product, BeeT, we intend to use toxins produced by Bacillus thuringiensis, also called BT toxins. However, these toxins are also harmful to our chassis, resulting in a reduction of toxin production when it is needed to kill the mites. To counteract this effect we envision the use of quorum sensing that activates BT toxin production only when there is a large quantity of BeeT present. Ideally, BeeT is able to produce BT toxin over a long period to improve its effectiveness against mites. However, if an entire population of BeeT is synchronized, we hypothesize that only a single burst of BT toxin will take place before both the BeeT and mites are killed. Thus, this system will not be maximally effective over long time periods. To accomplish this we need multiple sub populations of BeeT: some producing BeeT while others are recuperating.To help understand this complex system we use dynamic modelling.

Introduction

The quorum sensing system consists of two proteins LuxI and LuxR. LuxR is constitutively expressed, together with Acylated Homoserine Lactone (AHL) it forms a complex which activate the transcription of GFP ¹. In a natural system the complex LuxR-AHL controls transcription of LuxI, this is a positive feedback loop that increases the amount of AHL in the system ¹. When there is more AHL in the system, AHL is more likely to bind to the LuxR protein. AHL is a molecule that can diffuse freely through the cell membrane, and in this way diffuses from cell-to-cell. With the use of quorum sensing we tried to simulate these different populations. In figure 1 you can see how this system looks like.

What is quorum sensing?

Quorum sensing is a cell-cell communication system. The detection of chemical molecules allows the bacteria to distinguish between low and high cell densities. In this way the bacteria control gene expression in response to changes in cell number ^{1 2}. This process is achieved through the production and release of an autoinducer , in our case AHL. An autoinducer is a molecule that can diffuse through the cell membrane. The AHL can travel from one cell to the other. There are many different types of autoinducers in quorum sensing systems. . When sensed, the autoinducer can trigger other cells to produce more autoinducers.

According to the team from Davidson College and Missouri Western State University 2011 ³,negative feedback is present when LuxR protein is present and AHL-3OC6 is absent. They discovered that the BBa_K199052 part promotes "backwards transcription". In this research the importance of negative feedback in the quorum sensing system, to create different cell populations, has been investigated. As you can see in Figure 2

different populations can occur when the production rate of luxR and the complex forming are changed. However, when we remove the feedback in the system and change the same parameters, we get similar responses of the system. Shown in Figure 2.

This shows that the feedback has a minimal influence on the system to create different cell populations. Also when there is no negative feedback in the system parameter sets with more frequent higher GFP are obtained. See graphs in box below;

higher GFP

The figures are simulated with the GFP response obtained from the quorum sensing model, using the parameter sets that produce the best response. In the presence and absence of negative feedback in, the system different populations can occur by assuming the production rate of luxR and the production rate of the complex are different in each cell.
This system worked to control the LuxR production rate, however it could not be envisioned. Therefore, we designed another system that could help us create subpopulations downstream of the quorum sensing mechanism. Since the quorum sensing model does not give us a strong result in subpopulations, we extended the model with the subpopulation part. So it could be tested in the lab.

Why include a subpopulation system?

The subpopulation system consist of two genes; the first encodes for the protein that inhibits the systems expression, the other encodes for the corresponding activation protein of the system. The 434 cl-LVA inactivates the λ-cl directly, or prevents the translation of the λ-cl protein. This subpopulation system is based on the system Bokinsky uses ⁵

As you can see in Figure 3 glucose has a suppressing function on the system. Arabinose has a activating function on the system. We used the model to predict what will happen when we add glucose in different amounts. We tested a number of parameter sets and selected certain parameter sets based on the strength of glucose response. Within the heat map you can see in which ratios the initial amounts of λ and 434 in the system are needed to get high RFP production. Where glucose and arabinose have a fixed number.

When λ is present in large amounts, the RFP response will be high. You need a lot more λ than 434 to get high RFP responses. This can be expected when you look at the subpopulation system. The system is inhibited by 434, which represses the RFP production, and λ activates the RFP production. In figure 4 you can see that there is little difference between the 434 and λ amounts that are present for the output of RFP. This means that the initial conditions do not have much influence on the 434 and λ. With this particular parameter set, we can conclude that the translation rates are more important for the RFP response than the initial conditions. For other parameter sets the initial conditions are important. To understand how changing the RBS impacts RFP responses, we simulated the system for different combinations of transcription rates. From these simulations we found:

Combined system

We hypothesize the following: When there are more cells are present in the system, more AHL-LuxR complex is formed. The complex inhibits the subpopulation promoter. When the promoter is inhibited production of 434 will be suppressed and production of λ cl will be activated. At a certain time point λ-cl takes control over the system, because 434 has a higher turnover rate than λ-cl. In this case more λ-cl results in more RFP.

In a later stage, to get the desired response, the quorum sensing system and the subpopulation system were combined. As shown in Figure 4, you can see how we think to generate different cell populations. With this extended part we try to generate a system which predicts the behaviour of the subpopulations. In figure 6 you can see the increasing RFP over time after many cell divisions, indicating an increasing cell population.

Methods

During the research Matlab version R2016a has been used.
Because there was no data from the wet lab we assumed that all parameters exist within a biological seasonable range numbers between 0 and 1. Tuneable parameters are used, each parameter set can produce dramatically different population dynamics.To determine which of these parameters produce the best system response we used Latin Hypercube Latin hypercube is a statistical method to get random numbers from a box of x by n numbers. For example, if x = 4, where x is the number of divisions within the parameter value range, and n = 2, where n is number of parameters, you will obtain a box with 4 square times 2 square, giving you 24 random numbers. Within each division of the parameter space a single random number is chosen. sampling.

With the parameter numbers of the Latin Hypercube sampling we made parameter sets that are obtained from a lognormal distribution a lognormal distribution is a method to describe a probability, in this case the probability of a certain parameter set used to generate a high GFP response. with a parameter estimation based on Raue et al ⁴. Below you could see an example

With these confidence intervals Equation for confidence interval used the best parameter sets could be chosen.
EQUATIONS!!!!!

higher GFP

Light Kill Switch

To prevent BeeT from escaping into the environment around the hive and spreading we built a light kill switch. This system consists of a light switch that triggers when blue light hits the organisms. This light switch is coupled to a toxin/anti-toxin system, when the light switch is triggered it inhibits the production of the anti-toxin. With the anti-toxin production inhibited, the constitutive production of toxin kills BeeT. This system was also modeled using dynamic modelling, this was to ensure that the system only kills BeeT in the presence of blue light and it survives when no blue light is present.

Metabolic Modeling

In order to assess the real world viability of BeeT, we evaluated the proposed system of application by making a model of the entire system. To do this we used Flux Balance Analysis (FBA) to model the chassis. The chassis The chassis-organism is the framework that is modified for use in synthetic biology experiments. of BeeT is a variant of ​ Escherichia coli, for which it is known that it does not grow overnight in high osmotic pressure environments of 1 mol sucrose / liter or above. ¹ Supplementing an Apis mellifera (honey bee) colony with sugar water is a well established practice amongst beekeepers. ⁴ This is usually done with about 1 kg of table sugar for each liter of table sugarChemically speaking this is pure sucrose: C₁₂H₂₂O₁₁. After heating and stirring, this ends up as a concentration of 625 grams of sucrose per liter of water. This concentration can also be defined as 1.82 mol sucrose per liter, which is almost twice the threshold value at which E. coli would no longer grow. However, in this project we only need the ​E. coli to survive in the sugar water. This only needs to be as long as the worker bees require to pick the BeeT-cells up and bring them to where the Varroa destructor are. The question we try to answer is: does our chassis survive in the sugar water, and if so, for how long?

A model of E. coli survival

In order to make this model a technique called Flux Balance Analysis (FBA) was used to describe the relationship between the metabolism of the E. coli and the osmotic pressure of the sugar water. FBA is a mathematical method that can be used for predicting reaction fluxes and optimal growth rates of species using genome-scale reconstructions of their metabolic networks. In this case we used it to specifically predict the effect of water efflux on the total amount of ATP the cell has available for maintenance. The model used as a base for this analysis was iJO1366 ³from the BIGG database.

The relationship between maximum ATP available for survival and water efflux is shown in Figure 1 the results from the metabolic model suggest that there is a linear relationship between ATP availability and water efflux. This implies that if no water is available for ATP used for maintenance, the cell will die. When the model runs without any modification, i.e. in an environment where it is in the exponential growth phase, an ATP Maintenance flux of 3.15 mmol⋅gram Dry Weight^-1⋅hour^-1 is given as output by the model.

We do not know the exact amount of ATP needed for survival in sugar water conditions, but because of the results from Figure 1 we can start looking at the relationship between survival time and water efflux.

In Figure 2 we can see not only the relationship of survival time against max ATP available for survival, but also how different thresholds of minimal cell-water tolerance would affect this relationship. The minimal cell-water tolerance threshold gives the value at which percentage of the remaining cell-water reaches a point of no return for the cell. This has a drastic effect on survival time, changing 20 minutes of maximum survival time to a mere ~90 seconds in the worst case scenario.

Up to 90 minutes

Figure 1 shows us that osmotic pressure alone can indeed have an effect on cell regulation and cell death and from Figure 2 it appears that the minimal water allowance threshold has a high impact on range of possible times. Our model is limited in that it predicts an infinite survival time beyond 90 minutes. This suggests that our model may be missing some form of regulation that allows for longer survival times. To understand this effect, we shall conduct an experiment to test this prediction. This experiment can be found here.

What we can say is that if the cells can survive for longer outside of this period, then they must have enough ATP available for basic maintenance, and that if cell death occurs then other processes than pure water-efflux must be the cause of that. This could be due to a combination of lacking nutrients and water-efflux, or over production of osmolytes to keep the balance.

Beehave

Due to regulatory and experimental hurdles it is difficult to test the effectiveness of BeeT in combating Varroa destructor in the field. We would still like to be able to give advice to local beekeepers on the ideal application strategy of BeeT based on several scenarios. To accomplish this, the well-known model BEEHAVE was adapted to include the effect of BeeT on Varroa mite and virus dynamics in simulated colonies. BEEHAVE is an open-source, agent-based model which can be used to examine the multifactorial causes of Colony Collapse Disorder ^{1 2} . It consists of several modules which controls aspects like foraging, Varroa mite dynamics and colony growth (figure 1) and was extensively tested for robustness and realism ¹ .

BeeT can be transported into the hive by adding it to sugar water or bee bread. Each of these sources has its own advantages and disadvantages. In this project we have used Escherichia coli as our model chassis and this can be applied to sugar water [link to Ronald]. Sugar water is supplemented to support a colony during honey harvest and before winter as a substitute for nectar ^{3 4} . Supplementing Apis mellifera (honey bee) with sugar water is a well established and familiar practice amongst beekeepers ³ . However, reconstructing the same system in Lactobacillus species would allow us to use BeeT in bee bread. Bee bread has certain advantages over sugar water as it is more frequently transported to larvae. It is a combination of pollen, regurgitated nectar and glandular secretions and is inoculated with fermenting bacteria by honeybees ⁵ . There is mounting evidence that pollen supplementation increases protein content in honey bee hemolymph, likely improving survival of colonies to various stressors ^{6 7} .

BeeT Module

BEEHAVE is an open-source, GNU licensed agent-based model utilizing NetLogo and consists of several interlocking modules which each model different aspects of the beehive. BEEHAVE has the intended goal of modelling the wide variety of stresses affecting honey bees and is the only model incorporating all these different aspects ¹ . As such it is the ideal basis for our investigation into the effects of BeeT on Varroa mites and honey bee dynamics. BEEHAVE has several modules covering inter-colony dynamics, foraging and a Varroa mite model as depicted in Figure 1. Two viruses are also included in the model; deformed wing virus (DMV) and acute paralysis virus (APV) for which Varroa mites are a vector. Our BeeT module, which runs parallel to BEEHAVE, is capable of modeling transport of BeeT into the hive using sugar water or bee bread. It also calculates how much BeeT is transported to larvae based on consumption of respectively honey and pollen stores. Based on the amount of BeeT at larvae the Varroa mite mortality, when Varroa mites emerge from brood cells, is determined. This in turn affects Varroa mite population levels in the hive, reducing virus loads in the hive and allowing colony survival.