A constant low level of Cry toxin can facilitate resistances¹¹. This is why the toxin production should be regulated when BeeT spreads through the hive. We created two main systems that regulate the toxin production. One is a system designed with riboswitches that promote toxin production when Varroa mites are present. The other system, that works in parallel with the riboswitches, uses quorum sensing to start toxin production only when the concentration of BeeT is high.

Riboswitches are pieces of mRNA that can regulate gene expression depending on whether it is bound to a ligand. The ligands used here are guanine and vitamin B12. Both substances indicate the presence of Varroa mites. 95% of the mite faeces consist of guanine. Vitamin B12 is present in the haemolymph of the honey bees, which is the food source of Varroa mites. Both riboswitches are successfully built into a construct so that when the ligand is present, toxin can be produced. Furthermore, they have been tested with RFP as reporter gene in the presence of different concentrations of their corresponding ligand. The results for the vitamin B12 riboswitch are shown in Figure 5. As can be seen here, when the concentration of vitamin B12 increases, the RFP production increases as well.

The second regulatory system uses quorum sensing. A quorum sensing mechanism enables the bacteria to regulate their expression based on their density. We adopted the lux system originating from Vibrio fischeri and demonstrated this system’s functionality using a newly constructed GFP reporter (Figure 6). When the cell density increases, the cells will sense each other’s autoinducers. These induce production of more autoinducers and GFP.

When the cell density is high enough, the quorum sensing system ensures that more and more toxin is produced. The downside of this is that high toxin levels will likely kill the BeeT population. This is because the Cry toxin will lyse BeeT when produced in very high concentrations. It would be beneficial to divide the population of bacteria in toxin producers and non-toxin producers, to maintain a subpopulation of healthy bacteria. These cells will be able to initiate a new growth phase after death of the toxin-producing cells. This requires that cells respond to the stimuli at different times despite being genetically identical. To create such a system, we used two proteins: one encodes for the protein that inhibits the toxin expression, whereas the other promotes toxin expression. Depending on which protein is more present,that is more present toxin production is on or off. Both proteins are encoded behind the same promoter. However, one of the proteins has a higher turnover rate. The trick is to find the “sweet spot” of the translation rates at which in some cells one protein takes the upper hand ,and inr some cells the other protein. This sweet spot has been found with a mathematical model. Figure 7 shows the presence of two different subpopulations as computed by the model.

To further elaborate on the toxin production switches, we want to add a toggle switch to the system. Given the fact that BeeT might not grow very well in beehives, we decided to couple the toggle switch with a riboswitch. The potential slow growth rate is a disadvantage for the quorum sensing system, because these cells might not be able to grow to the density required for toxin production. The riboswitch, however, is not dependent on population density.
The toggle switch we created controls expression of the BeeT’s toxin between an off-state and an on-state. It is switched on by guanine or vitamin B12, and switched off by blue light. The latter combines the optogenetic kill switch, which will we explained later in more detail, with the toxin production. The toggle switch does not only combine multiple systems, but also ensures that the response to guanine or vitamin B12, and initiation of toxin production, are fast. In order to create this system, a new hybrid promoter had to be made. The hybrid promoter ensures that toxin production is only possible in the absence of light. Figure 8 shows the testing results of 5 different hybrid promoters. From this it can be concluded that the hybrid promoter BBa_K1913022 is the most active. Although we did not have time to test the system as a whole, we expect it to work since both the riboswitches and the hybrid promoter are functional.

BeeT is intended to use in beehives, where bee’s fly in and out continuously. This means BeeT can be spread by the bee’s throughout the environment. Since we cannot be sure about its effect on existing ecosystems, BeeT must be engineered to die if it leaves the beehive. To accomplish this we made use of a optogenetic-based kill switch and a cas9-based kill switch.

The optogenetic-based kill switch is the unification of two different genetic systems: a toxin-antitoxin system native to E. coli (MazEF), and an artificially-created promoter system activated by light (pDawn). The antitoxin MazE is constitutively expressed. The toxin MazF is only expressed in the presence of light, because MazF is regulated via pDawn. This means that in the darkness of the beehive, in which blue-light irradiance is close to zero, no toxin is produced, allowing the cell to remain stable. However, in sunlight toxin production takes the upper hand and the cell dies. Figure 9 demonstrates that the pDawn promoter system works. Alongside the pDawn we tested pDusk, a promoter system activated in the absence of light. pDusk. This promoter gave negative results.

The artificially-created promoter systems pDusk and pDawn were modelled in Matlab together with the MazEF toxin-antitoxin system. Within our parameter estimation procedure we found two parameter sets, out of 1000 sampled sets, which satisfy the conservative constraints described in the optogenetic kill switch modelling section. We fitted the model to literature data and can conclude that our model describes the system’s behaviour in the wet-lab. The results from these two sets can be seen in the animated Figure 10. The figure shows that it takes a few hours for the MazF toxin to take the upper hand in the pDawn system. This means that beekeepers can open their beehives during work, without immediately destroying the BeeT.

We added an additional kill switch, to bulletproof our biocontainment strategy. As a chassis for BeeT we wanted to use a bacterial strain developed by Mandell and colleagues (2014)¹. This “biocontainment strain” is auxotrophic for a synthetic amino acid, para-L-biphenylalanine (BipA). We aimed to complement this strain by adding a measure to prevent horizontal gene transfer. Our objective was to cleave heterologous DNA with a modified Cas9 as soon a BeeT runs out of BipA. When BipA is present, the synthetic amino acid should be built into the active site of Cas9, making it catalytically dead. However, in the absence of BipA, the native amino acid is incorporated, partially restoring cleaving activity. This active Cas9 will cut heterologous DNA. Figure 11 shows the Cas9 incorporated with BipA.

Testing BeeT in a Beehive BEEHAVE

Ideally we want to test BeeT in a beehive. This is, however, not a feasible option for this iGEM project. Allowing genetically modified organisms to be present in the environment is far from responsible, moreover forbidden. Because of this we had to find an alternative way to test BeeT. First we proved with an experiment and with a model that BeeT can survive in the sugar water, the medium used to apply BeeT to the bees. Secondly, we modelled the influence of BeeT in an open source model called BEEHAVE. We adapted the model in a way that it could predict what the effect of BeeT on virus epidemiology, mite population dynamics, and bee population dynamics is.

Using Flux Balance Analysis we describe the relationship between the metabolism of E. coli and the osmotic pressure of sugar water. From this we can predict how different thresholds of minimal cell-water tolerance will affect the relationship between the survival time and the maximum ATP available for survival (Figure 12). Our model predicts an infinite survival time beyond 90 minutes. We’ve proven in the lab that E. coli can survive at least 24 hours in sugar concentrations that are similar to sugar water for bees. Taking the model into account, we assume that E. coli will survive indefinitely in sugar water. This is taken into account in the BEEHAVE model.

We modelled the behavior in BEEHAVE mainly because we are interested in how BeeT can best be applied given certain assumptions. If no functional BeeT is applied to the hive, the bee population dynamics will follow the trend as shown in Figure 13a. In other words, the bee colony will collapse after four to five years. If functional but not 100% effective BeeT is applied, the bee population will shrink, and come in equilibrium with the mite population. (Figure 13b) If effective BeeT is applied to the hive, the mite population dies (Figure 13c).
Furthermore, BEEHAVE predicted when is the most efficient time to apply BeeT and how to apply it. As the results in Table 1 show, it is more effective to give the sugar water in the spring rather than in autumn. Secondly, it is more effective to apply BeeT via artificial beebread. This is a future application, since this would require to change the chassis into a Lactobacillus specie. Still when BeeT is applied in the sugar water and its ability to kill mites is high enough it is capable to bring down the mite population to 0, even when the starting population of mites is very high.

p.s. Remco is finishing the last figures