Difference between revisions of "Team:Wageningen UR/Results"
Tmswartjes (Talk | contribs) |
|||
| Line 101: | Line 101: | ||
</figcaption> | </figcaption> | ||
</figure> | </figure> | ||
| − | <p>The artificially-created promoter systems pDusk and pDawn were <a href="https://github.com/marioisbeck/iGEM_Wageningen_UR_2016">modelled in Matlab</a> together with the MazEF toxin-antitoxin system. | + | <p>The artificially-created promoter systems pDusk and pDawn were <a href="https://github.com/marioisbeck/iGEM_Wageningen_UR_2016">modelled in Matlab</a> together with the MazEF toxin-antitoxin system. We fitted the model to literature data and can conclude that <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Biocontainment#Lightswitch">our model </a> describes the system’s behaviour in the wet lab well for pDusk and pDawn. Within our parameter estimation procedure for the extended pDusk + const. mazF and pDawn + const. mazE systems, we found two parameter sets which satisfy the conservative constraints. This is described in the optogenetic kill switch modelling section. The results from these two sets can be seen in the animated Figure 10. With increasing light intensities, the response of MazE and MazF is plotted. This gives us an indication on where in parameter space we would need to focus on for future studies and to extend the model with further wet-lab experimental data. In addition, the Figure 10 indicates, that it takes a few hours for the MazF toxin to take the upper hand in the pDawn system. Backed up by literature data, we can assume, that the beekeepers can open their beehives during work, without immediately destroying BeeT.</p> |
<figure> | <figure> | ||
Revision as of 19:39, 19 October 2016
Results
In this section, the main results of the BeeT project are presented. BeeT is engineered to produce a toxin specific for Varroa destructor, produce the toxin on the right time, and remain vigilant inside the hive.
BeeT should only affect Varroa mites. To accomplish this we decided to make use of Cry toxins. These toxins are naturally produced by Bacillus thuringiensis and therefore also known as Bt toxins. A functional Cry toxin is only effective when specific binding occurs to the gut membrane of the target organism. After binding, the Cry toxins will form pores in the cell membrane, resulting in cell death. As cell death occurs, the gut membrane becomes porous, killing the organism1. To find a Cry toxin active against V. destructor we engineered our own toxins. We also searched for them in nature.
We developed an in vitro test for Cry toxins. This test is very relevant because testing toxins on Varroa mites is near to impossible. Due to their parasitic nature, mites in laboratory conditions die irrespectively of their treatment. For the new assay, we made Brush Border Membrane Vesicles (BBMV's) out of the membranes of Varroa. We incorporated 6-carboxyfluorescein in the vesicles. A functional Cry toxin will create pores into the BBMV's, which results in the leaking of fluorophores out of the BBMV's. Due to the self-quenching behaviour of 6-carboxyfluorescein, this can be measured as an increase in fluorescence. As a proof of principle, BBMV's from the gut of Tenebrio molitor were made and loaded with 6-carboxyfluorescein to test the pore formation ability of Cry3Aa, which is known to be toxic to T. molitor larvae 3. Figure 1a shows how the fluorescence of BBMV's incorporated with fluorophores increases in the presence and absence of Cry3Aa. Using the data, a kinetic value was calculated for the process. These values are shown in Figure 1b. From this it can be concluded that the presence of a functional Cry protein be measured.
For the engineering of a Cry toxin specific for V. destructor, we used the Cry3Aa toxin as a starting point. This Cry toxin consists of three domains, off which one is responsible for binding. In 1996, Rajamohan et al. demonstrated that mutations in the binding sites of Cry toxins can both decrease and enhance the specificity of a toxin towards its target organism2. Three putative binding sites have been identified after analysing the 3D structure of the binding domain of Cry3Aa. These putative sites were changed with random mutagenesis, and the adapted proteins were cloned into Escherichia coli. 144 Cry proteins were produced and tested for activity on BBMV's from V. destructor as described previously. After initial testing, 24 candidates were selected for further testing. These results are shown in Figure 2. It can be concluded that the third binding site, amino acids 410-416, is a good candidate for future engineering and specificity adaptation of this particular Cry toxin. Toxins Cry3.3.3 and Cry3.3.7 should not be taken into account due to their highly deviating reaction speeds.This leaves us with one proper candidate – the toxin mutant Cry3.3.5.
Phage display was performed in order to find specific motifs for binding to Varroa gut-membrane receptors. Phages with a binding motif on their exterior were exposed to the gut membrane of V. destructor. Subsequently, we isolated and analysed the bound phages. The filamentous bacteriophage M13 was used with a 12-mer library (The Ph.D.™-12 Phage Display Peptide Library). The phages were fed to Varroa mites and exposed to BBMV's originating from Varroa mites. We then isolated and sequenced the recovered phages. The consensus sequences of the binding motif of the 12-mer both in vivo and in vitro are shown in Figure 3.
Alongside creating a Cry toxin ourselves, we searched nature for existing cry toxins. We gathered 800 dead Varroa mites and from them isolated B. thuringiensis or related species, which might have been the cause of dead. Figure 4 shows the morphology of B. thuringiensis and two discovered strains. Five out of 106 isolates were identified as Bacillus-like species. One strain, not B. thuringiensis, showed the presence of a large over-expressed protein and was sent for sequencing. We reduced the dataset to four candidate genes with the toxin scanner. This is a great starting point for future research.
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 consists of guanine. Vitamin B12 is present in the haemolymph of the honey bees, and can be expected to leak into the hive when Varroa mites damage the honeybees. 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, toxin production is either 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 in 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.
We added a toggle switch to the system that allows BeeT to regulate toxin production even if the bacteria do not grow well in beehives. Slow growth is a limitation of the quorum sensing system: cells might not be able to grow to the density required for toxin production. Instead the toggle switch system makes use of the earlier described riboswitch, which 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 is based on the optogenetic kill switch, 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 was made. The hybrid promoter ensures that toxin production is only possible in the absence of light. Figure 8 shows 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 separately.
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 an 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, where blue-light irradiance is close to zero, no toxin is produced. This allows the cells 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 pDawn we tested pDusk, a promoter system activated in the absence of light. This promoter system did not provide a strong enough response to be useful for our intended purpose.
The artificially-created promoter systems pDusk and pDawn were modelled in Matlab together with the MazEF toxin-antitoxin system. We fitted the model to literature data and can conclude that our model describes the system’s behaviour in the wet lab well for pDusk and pDawn. Within our parameter estimation procedure for the extended pDusk + const. mazF and pDawn + const. mazE systems, we found two parameter sets which satisfy the conservative constraints. This is described in the optogenetic kill switch modelling section. The results from these two sets can be seen in the animated Figure 10. With increasing light intensities, the response of MazE and MazF is plotted. This gives us an indication on where in parameter space we would need to focus on for future studies and to extend the model with further wet-lab experimental data. In addition, the Figure 10 indicates, that it takes a few hours for the MazF toxin to take the upper hand in the pDawn system. Backed up by literature data, we can assume, that the beekeepers can open their beehives during work, without immediately destroying BeeT.
We added an additional kill switch, to reinforce 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 in 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 modeled 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 reach an equilibrium with the mite population. (Figure 13b) If effective BeeT is applied to the hive, the mite population dies (Figure 13c).
Furthermore, beehave predicted the most effective time and method to apply BeeT. As the results in Table 1 show, it is more effective to give BeeT-containing sugar water in spring rather than in autumn. Secondly, the model showed that application of BeeT is even more effective using a Lactobacillus species as chassis. This would allow application of BeeT via artificial ‘beebread’.
| Period and treatment | Colony death | Colony survival | Colony thriving |
|---|---|---|---|
| Sugar water, spring | 6,6% | 80,6% | 12,8% |
| Bee bread, spring | 0% | 2,9% | 97,1% |
| Sugar water, winter | 15,1% | 80,7% | 4,2% |
| Bee bread, winter | 0 | 57,6% | 42,4% |
