Latest revision as of 14:04, 4 November 2016

MENU ▤

Wageningen UR iGEM 2016

Overview

Description

Results

In this section, we present the main results of the BeeT-project. The BeeT-system is subdivided in three parts: specificity, regulation, and biocontainment. These parts are based on the main demands BeeT had to meet. To ensure BeeT's specificity, we engineered an existing toxin and identified a potential mite pathogen. Furthermore, we developed a toxicity-assay to facilitate future research on Varroa destructor-specific toxins. Also, software was made to aid in the discovery of Varroa destructor-specific toxins. For the regulation of toxin production, we developed two riboswitches that can detect mites, designed a genetic circuit which regulates the formation of BeeT subpopulations, and included a toggle switch which ensures BeeT is only functional in the beehive. BeeT's biocontainment was ensured through the introduction of an optogenetic kill switch and a Cas9 kill switch. We also modeled various parts of BeeT: the optogenetic kill switch, the subpopulation dynamics circuit, the effects of the method of application on the chassis, and the effect of BeeT on mite and bee population dynamics.

BeeT should only affect Varroa mites. To accomplish this, we decided to use 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 death¹. To find a Cry toxin active against V. destructor we engineered our own toxins, and also searched for them in nature.

Because testing toxins on Varroa mites in vivo is near to impossible, we developed an in vitro test for Cry toxins. Due to their parasitic nature, mites in laboratory conditions are very fragile and frequently die irrespectively of their treatment. For the new assay, we made Brush Border Membrane Vesicles (BBMV's) out of the membranes of Varroa mite. The vesicles were loaded with 6-carboxyfluorescein. Any 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, the leaking 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 larvae². Figure 1a shows how the fluorescence of BBMV's incorporated with fluorophores increases in the presence and absence of Cry3Aa. The kinetic value was calculated for the process, which are shown in Figure 1b. From this we concluded that the in vitro toxicity assay can be used to test for functionality of Cry toxins.

Figure 1. (a) The fluorescence of two solutions with BBMV's obtained from T. molitor incorporated with fluorophores were measured over time, one in the presence and one in the absence of Cry3Aa. (b) The reaction rate constants for six individual measurements were calculated with the equation: fluorescence_t=fluorescence_(t=∞)-fluoresence_(t=∞)∙e^(-k∙t)+fluorescence_(t=0).

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. Rajamohan et al. (1996) demonstrated that mutations in the binding sites of Cry toxins can both decrease and enhance the specificity of a toxin towards its target organism³. 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. The results are shown in Figure 2. It can be concluded that the third binding site, consisting of 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.

Figure 2. Heatmap of the reaction constants relative to the blank. A higher value indicates a higher toxicity and specificity of the tested toxin.

Phage display was performed in order to find specific motifs for binding to Varroa gut-membrane receptors. Phages (filamentous bacteriophage M13) with a binding motif (The Ph.D.™-12 Phage Display Peptide Library) on their exterior were exposed to the gut membrane of V. destructor by feeding the phages to Varroa mites. Subsequently, we isolated and sequenced the recovered phages. The consensus sequence of the binding motif of the 12-mer is shown in Figure 3.

Figure 3. Consensus sequence of recovered phages in in vivo Phage Display in V. destructor. The legend shows which aminoacid has which properties. The letters "N" and "C" in the graph indicate the N-terminus and the C-terminus, respectively.

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 death. Figure 4 shows the morphology of B. thuringiensis and two strains we discovered. Five out of 106 isolates were identified as Bacillus-like species. One strain, which turned out to be 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.

Figure 4. Microscopy images of Coomassie-stained isolates, 1000x magnification with a Zeiss Axio Scope.A1 brightfield microscope. (a) B. thuringiensis HD350. The red arrow points to a Cry toxin, the green arrow to a spore, and the yellow arrow to a vegetative cell. (b) Isolate 62, a coccus. Most isolates had this morphology. (c) Isolate 82, showing Bacillus-like morphology.

A constant low level of Cry toxin can facilitate resistances⁴. 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 density of BeeT is high.

Riboswitches are parts of mRNA that can regulate gene expression depending on whether a ligand is bound. The ligands we used 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 were constructed successfully so that when the ligand is present, toxin can be produced. 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, RFP production increases with increasing vitamin B12 concentration.

Figure 5. E. coli with the vitamin B12 riboswitch coupled to a RFP output was grown overnight in the presence of different concentration vitamin B12. (a) The fluorescence divided by OD over time is shown for different concentrations vitamin B12. (b) The relation between the fluorescence divided by OD after 12 hours incubations and different concentrations of vitamin B12.

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 cell density increases, the cells will sense each other’s autoinducers. This induces production of more autoinducers and GFP.

Figure 6. Fluorescence and absorbance data for E. coli quorum sensing strains. The continuous lines represent the fluorescence divided by OD600. The dashed lines represent the absorbance at 600 nm. The red and green lines represent quorum sensing strains, the purple strain has a reporter plasmid only. For both strains every value displayed is the average of at least three technical replicates, and for each the line displayed is one of three biological repeats of which all show a similar pattern.

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.

Figure 7. Two populations are visible and together they form a growing cell population. The right y-axis shows the volume of these populations. Volume oscillations correspond dividing cells. The total amount of RFP produced by the toxin is shown by the black line.

As an alternative to quorum sensing, 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. Apart from combining multiple systems, the toggle switch ensures that the response to guanine or vitamin B12 is fast. 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 the results of 5 different hybrid promoters controlling expression of RFP. From this we concluded that the hybrid promoter BBa_K1913025 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.

Figure 8. For five E. coli cultures, each with a different hybrid promoter and all with the reporter gene RFP, the fluorescence divided by OD 600 is shown. The cultures were grown overnight in the dark. As a negative control, the same E. coli with the corresponding hybrid promoter but lacking the light sensor was used.

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 kill switch and a Cas9 kill switch.

The optogenetic 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 toxin MazF is only expressed in the presence of light, because MazF is regulated via pDawn. The antitoxin MazE is constitutively expressed, to protect the cell against leaky expression of MazF. 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.

Figure 9. Response of pDawn- and pDusk-expressing E. coli to intense blue light (equivalent to the component in direct sunlight) and total darkness. Left: cell pellets. Right: fluorescence over OD600. Fluorescent protein mCherry is used as a reporter.

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 our focus should be for future studies and how the model should be extended with further wet-lab experimental data. In addition, 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.

Figure 10. Response of the two parameter sets which satisfy the conservative constraints. The light intensity is changing each moving step. The legend in pDusk is also valid for pDawn. The letter 'e' resembles the antitoxin MazE, whereas 'f' resembles the toxin MazF

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)⁵. 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. We managed to incorporate BipA in Cas9, which is shown in Figure 11.

Figure 11. SDS-PAGE of fractions after FPLC purification of Cas9 with incorporated BipA (a) and a negative control (b). The expected size of Cas9 is 156 kDa. Marker: Precision Plus protein ladder (Bio-Rad).

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.

Figure 12. The relationship between the maximum ATP available for survival for an E. coli in a sugar-water environment and the theoretical survival time, given a constant water efflux over this time and a starting volume of 2.8e^-13 grams. The various coloured lines indicate water tolerance thresholds for the E. coli

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).

Figure 13. The honey bee population is shown in blue and the Varroa mite population in red. A: Colony rapidly declines when no BeeT is present. Starting population is 20 Varroa B: Colony barely survives Varroa mite infestation. Shows Varroa mite in red and worker bee population in blue. Starting population is 20 Varroa. C: Colony thrives regardless of Varroa mite infestation. Starting population is 20 Varroa mites. D: Colony thrives regardless of heavy Varroa mite infestation. Starting population is 10.000 Varroa mites.

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’.

Table 1. Three parameter sets per treatment, representing: colony death, survival, and thriving. If colonies can survive and thrive with higher degradation of BeeT (in-hive and outside the hive) and a lower effect of BeeT on Varroa mite mortality, it indicates a more effective treatment.

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%

References

1.Bravo, A., Gill, S. S., & Soberon, M. (2007). Mode of action of Bacillus thuringiensis Cry and Cyt toxins and their potential for insect control. Toxicon, 49(4), 423-435.↩

2. Jeff Fabrick, Cris Oppert, Marce´ D. Lorenzen, Kaley Morris, Brenda Oppert, and Juan Luis Jurat-Fuentes. A Novel Tenebrio molitor Cadherin Is a Functional Receptor for Bacillus thuringiensis Cry3Aa Toxin. The Journal of Biological Chemistry VOL. 284, NO. 27, pp. 18401–18410, July 3, 2009. ↩

3. Rajamohan F, Alzate O, Cotrill JA, Curtiss A, Dean DH. Protein engineering of Bacillus thuringiensis δ-endotoxin: Mutations at domain II of CryIAb enhance receptor affinity and toxicity toward gypsy moth larvae. Proceedings of the National Academy of Sciences of the United States of America. 1996;93(25):14338-14343. ↩

4. Goodwin, M., & Van Eaton, C. (2001). Control of Varroa. A guide for New Zealand Beekeepers. New Zealand Ministry of Agriculture and Forestry (MAF). Wellington, New Zealand. ↩

5. Mandell, D. J., Lajoie, M. J., Mee, M. T., Takeuchi, R., Kuznetsov, G., Norville, J. E., ... & Church, G. M. (2015). Biocontainment of genetically modified organisms by synthetic protein design. Nature, 518(7537), 55-60. ↩

Difference between revisions of "Team:Wageningen UR/Results"

Latest revision as of 14:04, 4 November 2016

Results

Results

Testing BeeT in a Beehive Beehave

References

@@ Line 14: / Line 14: @@
 <li class="menu-item">
 <a href="#section4">Testing BeeT</a>
+<li class="menu-item">
+<a href="#references">References</a>
 </html>
 {{Wageningen_UR/menu}}
@@ Line 19: / Line 21: @@
 <section id="section1">
 <h1><b>Results<b></h1>
-<p>In this section we like to present you the main results of the BeeT project. BeeT is engineered to produce a toxin specific for <i>Varroa destructor</i>, produce the toxin on the right time, and be incapable of escaping the hive alive.
+<p>In this section, we present the main results of the BeeT-project. The BeeT-system is subdivided in three parts: specificity, regulation, and biocontainment. These parts are based on the main demands BeeT had to meet. To ensure BeeT's specificity, we engineered an existing toxin and identified a potential mite pathogen. Furthermore, we developed a toxicity-assay to facilitate future research on <i>Varroa destructor</i>-specific toxins. Also, software was made to aid in the discovery of <i>Varroa destructor</i>-specific toxins. For the regulation of toxin production, we developed two riboswitches that can detect mites, designed a genetic circuit which regulates the formation of BeeT subpopulations, and included a toggle switch which ensures BeeT is only functional in the beehive. BeeT's biocontainment was ensured through the introduction of an optogenetic kill switch and a Cas9 kill switch. We also modeled various parts of BeeT: the optogenetic kill switch, the subpopulation dynamics circuit, the effects of the method of application on the chassis, and the effect of BeeT on mite and bee population dynamics.</p>
 <figure>
 <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Specificity">
 <img src="https://static.igem.org/mediawiki/2016/5/51/T--Wageningen_UR--specifitytitle.jpg"></a>
 </figure><br/>
-<p>In order to improve on existing methods, BeeT should only affect <i>Varroa</i> mites. To accomplish this we decided to make use of Cry toxins. These toxins are naturally produced by <i>Bacillus thuringiensis</i> 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 into the cell membrane, resulting in cell death. As cell death occurs, the gut membrane becomes porous. Consequently, the organism dies. <sup><a href="#re1" id="refre1">1</a></sup> To find a Cry toxin active against <i>V. destructor</i> we engineered our own toxins. We also searched for them in nature.<br><br>
+<p>BeeT should only affect <i>Varroa</i> mites. To accomplish this, we decided to use Cry toxins. These toxins are naturally produced by <i>Bacillus thuringiensis</i> 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<sup><a href="#res1" id="refres1">1</a></sup>. To find a Cry toxin active against <i>V. destructor</i> we engineered our own toxins, and also searched for them in nature.</p>
-Due to the parasitic nature of <i>Varroa</i> mites, testing Cry toxins proved to be very problematic. To overcome this problem we developed an <i>in vitro</i> test for Cry toxins. Out of the membranes of the target organism, we made Brush Border Membrane Vesicles (BBMV's) incorporated with 6-carboxyfluorescein. 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 <i>Tenebrio molitor</i> were made and loaded with 6-carboxyfluorescein to test the pore formation ability of Cry3Aa, which is known to be toxic to <i>T. molitor</i> larvae <sup><a href="#jz3" id="refjz3">3</a></sup>.
-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 could be 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 results can be measured.</p>
+<p>Because testing toxins on <i>Varroa</i> mites <i>in vivo </i>is near to impossible, we developed an <i>in vitro</i> test for Cry toxins.  Due to their parasitic nature, mites in laboratory conditions are very fragile and frequently die irrespectively of their treatment. For the new assay, we made Brush Border Membrane Vesicles (BBMV's) out of the membranes of <i>Varroa</i> mite. The vesicles were loaded with 6-carboxyfluorescein. Any 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, the leaking can be measured as an increase in fluorescence. As a proof of principle, BBMV's from the gut of <i>Tenebrio molitor</i> were made and loaded with 6-carboxyfluorescein to test the pore formation ability of Cry3Aa, which is known to be toxic to <i>T. molitor</i> larvae<sup><a href="#res2" id="refres2">2</a></sup>.
+Figure 1a shows how the fluorescence of BBMV's incorporated with fluorophores increases in the presence and absence of Cry3Aa. The kinetic value was calculated for the process, which are shown in Figure 1b. From this we concluded that the <i>in vitro</i> toxicity assay can be used to test for functionality of Cry toxins.</p>
 <figure>
 <img src="https://static.igem.org/mediawiki/2016/c/c7/T--Wageningen_UR--resultsvesicles.jpg">
-<figcaption>Figure 1. (a) The fluorescence of two solutions with BBMV's obtained from <i>T. molitor</i> incorporated with fluorophores were measured over time. One in the presence and one in the absence of Cry3Aa. (b) The reaction rate constants for six individual measurements were calculated with the equation: fluorescence<sub>t</sub>=fluorescence<sub>(t=∞)</sub>-fluoresence<sub>(t=∞)</sub>∙e<sup>(-k∙t)</sup>+fluorescence<sub>(t=0)</sub>. </figcaption>
+<figcaption>Figure 1. (a) The fluorescence of two solutions with BBMV's obtained from <i>T. molitor</i> incorporated with fluorophores were measured over time, one in the presence and one in the absence of Cry3Aa. (b) The reaction rate constants for six individual measurements were calculated with the equation: fluorescence<sub>t</sub>=fluorescence<sub>(t=∞)</sub>-fluoresence<sub>(t=∞)</sub>∙e<sup>(-k∙t)</sup>+fluorescence<sub>(t=0)</sub>. </figcaption></figure>
-<p>For the <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Specificity#ToxinEngineering ">engineering of a Cry toxin </a> specific for <i>V. destructor</i>, 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 <i>et al</i>. demonstrated that mutations in the binding sites of Cry toxins can both decrease and enhance the specificity of a toxin towards its target organism<sup><a href="#lm2" id="reflm2">2</a></sup>. 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 <i>E. coli</i>. 144 Cry proteins were produced and tested for activity on BBMV's from <i>V. destructor</i> 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.
+<p>For the <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Specificity#ToxinEngineering ">engineering of a Cry toxin</a> specific for <i>V. destructor</i>, we used the Cry3Aa toxin as a starting point. This Cry toxin consists of three domains, off which one is responsible for binding. Rajamohan <i>et al</i>. (1996) demonstrated that mutations in the binding sites of Cry toxins can both decrease and enhance the specificity of a toxin towards its target organism<sup><a href="#res3" id="refres3">3</a></sup>. 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 <i>Escherichia coli</i>. 144 Cry proteins were produced and tested for activity on BBMV's from <i>V. destructor</i> as described previously. After initial testing, 24 candidates were selected for further testing. The results are shown in Figure 2.  It can be concluded that the third binding site, consisting of 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.
-</figure><br/>
+<br/>
 <figure>
 <img src="https://static.igem.org/mediawiki/2016/9/99/T--Wageningen_UR--LMheatmap.jpg">
 <figcaption>Figure 2. Heatmap of the reaction constants relative to the blank. A higher value indicates a higher toxicity and specificity of the tested toxin. </figcaption>
 </figure><br/>
-<p><a href="https://2016.igem.org/Team:Wageningen_UR/Description/Specificity#engineering2 ">Phage display</a> was performed in order to find the right specific binding motif. Phages with a binding motif on their exterior were exposed to the gut membrane of <i>V. destructor</i>. Hereafter, the bound phages were isolated and analysed. The filamentous bacteriophage M13 was used with a 12-mer library (The Ph.D.™-12 Phage Display Peptide Library). The phages were fed to <i>Varroa</i> mites and exposed to BBMV's originating from <i>Varroa</i> mites. The recovered phages were isolated and sequenced. The consensus sequences of the binding motif of the 12-mer both <i>in vivo</i> and <i>in vitro</i> are shown in Figure 3.</p>
+<p><a href="https://2016.igem.org/Team:Wageningen_UR/Description/Specificity#engineering2 ">Phage display</a> was performed in order to find specific motifs for binding to <i>Varroa</i> gut-membrane receptors. Phages (filamentous bacteriophage M13) with a binding motif (The Ph.D.™-12 Phage Display Peptide Library) on their exterior were exposed to the gut membrane of <i>V. destructor</i> by feeding the phages to <i>Varroa</i> mites. Subsequently, we isolated and sequenced the recovered phages. The consensus sequence of the binding motif of the 12-mer is shown in Figure 3.</p>
 <figure>
 <img src="https://static.igem.org/mediawiki/2016/a/a2/T--Wageningen_UR--PhageDisplayConsensusSequence.jpg">
-<figcaption>Figure 3. Consensus sequence of recovered phages in (a) <i>in vivo</i> phage display, (b) <i>in vitro</i> phage display, and (c) the combined results. The legend shows which amino acid has which properties. The letters “N” and “C” in the graph indicate the N-terminus and the C-terminus of the protein respectively.</figcaption>
+<figcaption>Figure 3. Consensus sequence of recovered phages in <i>in vivo</i> Phage Display in <i>V. destructor</i>. The legend shows which aminoacid has which properties. The letters "N" and "C" in the graph indicate the N-terminus and the C-terminus, respectively.</figcaption>
-</figure><br/>
+</figure>
-<p> Alongside creating a Cry toxin ourselves, we <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Specificity#Isolates">searched nature</a> for existing cry toxins. We gathered 800 dead Varroa mites and looked inside the mites for B. <i>thuringiensis</i> or related species, which might have been the cause of dead. Figure 4 shows the morphology of <i>B. thuringiensis</i> and two discovered strains. Five out of 106 isolates were identified as <i>Bacillus</i>-like species. One strain, not <i>B. thuringiensis</i>, showed the presence of a large over-expressed protein and was sent for sequencing. We analysed the sequence with our toxin scanner and aligned it to multiple databases, but were not able to identify the large protein. We need more experimental verification.</p>
+<p> Alongside creating a Cry toxin ourselves, we <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Specificity#Isolates">searched nature</a> for existing Cry toxins. We gathered 800 dead <i>Varroa</i> mites and from them isolated <i>B. thuringiensis</i> or related species, which might have been the cause of death. Figure 4 shows the morphology of <i>B. thuringiensis</i> and two strains we discovered. Five out of 106 isolates were identified as <i>Bacillus</i>-like species. One strain, which turned out to be not <i>B. thuringiensis</i>, 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.</p>
 <figure>
@@ Line 61: / Line 66: @@
 </figure><br/>
-<p>A constant low level of Cry toxin can facilitate resistances<sup><a href="#fn11" id="ref11">11</a></sup>. 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 <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Regulation#DetectingMites ">riboswitches</a> that promote toxin production when <i>Varroa</i> 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.</p>
+<p>A constant low level of Cry toxin can facilitate resistances<sup><a href="#res4" id="refres4">4</a></sup>. 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 <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Regulation#DetectingMites ">riboswitches</a> that promote toxin production when <i>Varroa</i> mites are present. The other system, that works in parallel with the riboswitches, uses quorum sensing to start toxin production only when the density of BeeT is high.</p>
-<p>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 <i>Varroa</i> 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 <i>Varroa</i> 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.</p>
+<p>Riboswitches are parts of mRNA that can regulate gene expression depending on whether a ligand is bound. The ligands we used are guanine and vitamin B12. Both substances indicate the presence of <i>Varroa</i> 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 <i>Varroa</i> mites damage the honeybees. Both riboswitches were constructed successfully so that when the ligand is present, toxin can be produced. 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, RFP production increases with increasing vitamin B12 concentration.</p>
 <figure>
-<img src="https://static.igem.org/mediawiki/2016/c/c9/T--Wageningen_UR--VatiminB12Ribo.jpg"> <figcaption>Figure 5. <i>Escherichia coli </i> with the vitamin B12 riboswitch coupled to a RFP output was grown overnight in the presence of different concentration vitamin B12. (a) The fluorescence divided by OD over time is shown for different concentrations vitamin B12. (b) The relation between the fluorescence divided by OD after 12 hours incubations and different concentrations of vitamin B12.</figcaption>
+<img src="https://static.igem.org/mediawiki/2016/c/c9/T--Wageningen_UR--VatiminB12Ribo.jpg"> <figcaption>Figure 5. <i>E. coli </i> with the vitamin B12 riboswitch coupled to a RFP output was grown overnight in the presence of different concentration vitamin B12. (a) The fluorescence divided by OD over time is shown for different concentrations vitamin B12. (b) The relation between the fluorescence divided by OD after 12 hours incubations and different concentrations of vitamin B12.</figcaption>
 </figure><br/>
-<p>The second regulatory system uses <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Regulation#QuorumSensing ">quorum sensing</a>. A quorum sensing mechanism enables the bacteria to regulate their expression based on their density. We adopted the lux system originating from <i>Vibrio fischeri</i> 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.</p>
+<p>The second regulatory system uses <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Regulation#QuorumSensing ">quorum sensing</a>. A quorum sensing mechanism enables the bacteria to regulate their expression based on their density. We adopted the lux system originating from <i>Vibrio fischeri</i> and demonstrated this system’s functionality using a newly constructed GFP reporter (Figure 6). When cell density increases, the cells will sense each other’s autoinducers. This induces production of more autoinducers and GFP.</p>
 <br><figure>
 <img src=" https://static.igem.org/mediawiki/2016/6/62/T--Wageningen_UR--QuorumSensingLab.jpg "> <figcaption>Figure 6. Fluorescence and absorbance data for E. coli quorum sensing strains. The continuous lines represent the fluorescence divided by OD600. The dashed lines represent the absorbance at 600 nm. The red and green lines represent quorum sensing strains, the purple strain has a reporter plasmid only. For both strains every value displayed is the average of at least three technical replicates, and for each the line displayed is one of three biological repeats of which all show a similar pattern.</figcaption>
 </figure><br/>
-<p>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 <a href="https://2016.igem.org/Team:Wageningen_UR/Model#QS_combined ">model</a>. Figure 7 shows the presence of two different subpopulations as computed by the model.</p>
+<p>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 <a href="https://2016.igem.org/Team:Wageningen_UR/Model#QS_combined ">model</a>. Figure 7 shows the presence of two different subpopulations as computed by the model.</p>
 <figure>
 <img src="https://static.igem.org/mediawiki/2016/c/c7/T--Wageningen_UR--QuorumSensingModelResults.jpg">
@@ Line 79: / Line 85: @@
 </figure><br/>
 <br>
-<p>To elaborate further on the toxin production switches, we wanted to add a <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Regulation#Toggleswitch ">toggle switch</a> to the system. Looking upon 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 in disadvantage for the quorum sensing system, because they may not be able to grow to the density required for toxin production. Whereas the riboswitch is not dependent on population density. <br> 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 optogenic kill switch, which will we explained later in more detail, with the toxin production. The toggle switch does not only combine multiple sysmtems, but also ensures that the response to guanine or vitamin B12 and initiation of toxin production are fast. In order to create the system, a new hybrid promoter had to be made. The hybrid promoters ensures that toxin production is only possible in the dark. Figure 8 shows the testing results of 5 different hybrid promoters. From this can be concluded that the hybrid promoter BBa_K1913022 functions the best. Although we did not have time to test the system as a whole, we expect it to work since both the riboswitches as the hybrid promoter are functional.</p>
+<p>As an alternative to quorum sensing, we added a <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Regulation#Toggleswitch ">toggle switch</a> 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. <br> 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. Apart from combining multiple systems, the toggle switch ensures that the response to guanine or vitamin B12 is fast. 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 the results of 5 different hybrid promoters controlling expression of RFP. From this we concluded that the hybrid promoter <a href="http://parts.igem.org/Part:BBa_K1913025">BBa_K1913025</a> 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.</p>
 <br><figure>
-<img src="https://static.igem.org/mediawiki/2016/6/62/T--Wageningen_UR--hybridpromoter2.jpg"> <figcaption>Figure 8. For five <i>E. coli</i> cultures, each with a different hybrid promoter and all with the reporter gene RFP, the fluorescence divided by OD 600 is shown. The cultures were grown overnight in the dark. As a negative control the same <i>E. coli </i> with the corresponding hybrid promoter, but lacking the light sensor, were used.</figcaption>
+<img src="https://static.igem.org/mediawiki/2016/6/62/T--Wageningen_UR--hybridpromoter2.jpg"> <figcaption>Figure 8. For five <i>E. coli</i> cultures, each with a different hybrid promoter and all with the reporter gene RFP, the fluorescence divided by OD 600 is shown. The cultures were grown overnight in the dark. As a negative control, the same <i>E. coli </i> with the corresponding hybrid promoter but lacking the light sensor was used.</figcaption>
 </figure><br/>
 </section id>
@@ Line 90: / Line 96: @@
 <img src="https://static.igem.org/mediawiki/2016/9/9f/T--Wageningen_UR--safetytitle.jpg"></a>
 </figure><br/>
-<p>BeeT is intended to use in beehives where bees fly in and out continuously. Hereby it can be spread throughout the environment. Since we cannot be sure about the effect on existing ecosystems if BeeT would be released in the environment, it must be engineered to die if it leaves the beehive. To accomplish this we made use of a optogenetic switch and a cas9 kill switch.</p>
+<p>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 kill switch and a Cas9 kill switch.</p>
-<p>The optogenetic kill switch is the unification of two different genetic systems: pDawn, an artificially-created promoter system activated by light; and MazEF, a toxin-antitoxin system native to <i>E. coli</i> . 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 - we confirmed instrumentally that the blue-light irradiance in a beehive is practically zero - no toxin is produced, allowing the cell to remain stable. However, in the sunlight, toxin production takes the upper hand and the cell dies.  In Figure 9 is demonstrated that the pDawn promoter system works. Alongside the artificially-created promoter system pDawn, we tested another one pDusk (a promoter system activated in dark), which gave negative results in the lab.</p>
+<p>The optogenetic kill switch is the unification of two different genetic systems: a toxin-antitoxin system native to <i>E. coli</i> (MazEF), and an artificially-created promoter system activated by light (pDawn). The toxin MazF is only expressed in the presence of light, because MazF is regulated via pDawn. The antitoxin MazE is constitutively expressed, to protect the cell against leaky expression of MazF. 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.</p>
 <figure>
@@ Line 100: / Line 106: @@
 </figcaption>
 </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. Within our parameter estimation procedure we found two parameter sets, out of 1000 sampled sets, which satisfy the conservative constraints described in the <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Biocontainment#Lightswitch">optogenetic kill switch</a> modelling section. We fitted the model to literature data and thus can conclude that our model describes the behaviour in the lab. The results from those two sets can be seen in the animated Figure 10. Taking a closer look, it can be seen 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 and work with their beehives, without immediately destroying the BeeT.</p>
+<p>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 <a href="https://2016.igem.org/Team:Wageningen_UR/Model#light">optogenetic kill switch modelling section</a>. 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 our focus should be for future studies and how the model should be extended with further wet-lab experimental data. In addition, 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>
 <img src="https://static.igem.org/mediawiki/2016/8/85/T--Wageningen_UR--mazEF_Response.gif">
-<figcaption>Figure 10. Response of the two parameter sets which satisfy the conservative constraints. The legend in pDusk is also valid for pDawn. The letter e resembles the antitoxin MazE, whereas f resembles the the toxin MazF</figcaption>
+<figcaption>Figure 10. Response of the two parameter sets which satisfy the conservative constraints. The light intensity is changing each moving step. The legend in pDusk is also valid for pDawn. The letter 'e' resembles the antitoxin MazE, whereas 'f' resembles the toxin MazF</figcaption>
 </figure><br/>
-<p>To bulletproof our biocontainment strategy, we added an additional <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Biocontainment#SAA ">kill switch</a>. As a chassis for BeeT we wanted to use a bacterial strain developed by Mandell and colleagues (2014)<sup><a href="#bk1" id="refbk1">1</a></sup>. 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 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. Unfortunately, we were not able perform <i>in vivo experiments</i> with this Cas9.</p>
+<p>We added an additional <a href="https://2016.igem.org/Team:Wageningen_UR/Description/Biocontainment#SAA ">kill switch</a>, to reinforce our biocontainment strategy. As a chassis for BeeT we wanted to use a bacterial strain developed by Mandell and colleagues (2014)<sup><a href="#res5" id="refres5">5</a></sup>. 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. We managed to incorporate BipA in Cas9, which is shown in Figure 11.</p>
 <figure>
 <img src="https://static.igem.org/mediawiki/2016/3/36/T--Wageningen_UR--resultsCas9.jpg">
@@ Line 114: / Line 120: @@
 </section id>
 <section id="section4">
-<h1><b>Testing BeeT in <strike> a Beehive</strike>  BEEHAVE<b></h1>
+<h1><b>Testing BeeT in <strike> a Beehive</strike>  Beehave<b></h1>
-<p>Ideally we want to test BeeT in a beehive. This is, however, not an option. Bringing genetically modified organisms free 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, via which it will be applied 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.</p>
+<p>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.</p>
-<p>With a technique called <a href="https://2016.igem.org/Team:Wageningen_UR/Model#metabolic ">Flux Balance Analysis</a> we describe the relationship between the metabolism of the <i>E. coli</i> and the osmotic pressure of the sugar water. From this we could predict how different thresholds of minimal cell-water tolerance would affect the relationship between the survival time and the maximum ATP available for survival (Figure 12). 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. Because of this, more evidence is needed to see what would happen to the <i>E. coli</i> in the sugar water for the time scales we are interested in. We <a href="https://2016.igem.org/Team:Wageningen_UR/ecoli_survival ">proved in the lab</a> that <i>E. coli</i> can survive at least 24 hours in sugar concentrations that are similar to sugar water for bees. This result was taken into account in the BEEHAVE model</p>
+<p>Using <a href="https://2016.igem.org/Team:Wageningen_UR/Model#metabolic ">Flux Balance Analysis</a> we describe the relationship between the metabolism of <i>E. coli</i> 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 <a href="https://2016.igem.org/Team:Wageningen_UR/ecoli_survival ">proven in the lab</a> that <i>E. coli</i> 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 <i>E. coli</i> will survive indefinitely in sugar water. This is taken into account in the beehave model.</p>
 <figure>
 <img src="https://static.igem.org/mediawiki/2016/4/45/T--Wageningen_UR--MetabolicModelRonald.jpg">
-<figcaption>Figure 12. The relationship between the maximum ATP available for survival for an <i>E. coli</i> in a sugar-water environment and the theoretical survival time, given a constant water efflux over this time and a starting volume of 2.8e<sup>-13</sup> grams. 2 The various coloured lines indicate water tolerance thresholds for the <i>E. coli</i></figcaption>
+<figcaption>Figure 12. The relationship between the maximum ATP available for survival for an <i>E. coli</i> in a sugar-water environment and the theoretical survival time, given a constant water efflux over this time and a starting volume of 2.8e<sup>-13</sup> grams. The various coloured lines indicate water tolerance thresholds for the <i>E. coli</i></figcaption>
 </figure><br/>
-<p>We modelled the behavior in <a href="https://2016.igem.org/Team:Wageningen_UR/Model#MM_model ">BEEHAVE </a> mainly because We are interested in how BeeT can best be applied given certain <a href="https://2016.igem.org/Team:Wageningen_UR/Model#MM_intro">assumptions</a>. If no or 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 year. If functional, but not 100% effective BeeT is applied, the bee population will shrink, but maintain. Eventually it will be in equilibrium with the mite population. (see Figure 13b) If effective BeeT is applied to the hive, the mite population dies (see Figure 13c). <br>
+<p>We modelled the behavior in <a href="https://2016.igem.org/Team:Wageningen_UR/Model#MM_model ">beehave </a> mainly because we are interested in how BeeT can best be applied given certain <a href="https://2016.igem.org/Team:Wageningen_UR/Model#MM_intro">assumptions</a>. 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). </p>
-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 <i>Lactobacillus</i> 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>
 <figure>
-<img src="https://static.igem.org/mediawiki/2016/f/f5/T--Wageningen_UR--highMite.jpg">
+<img src="https://static.igem.org/mediawiki/2016/1/19/T--Wageningen_UR--combined.jpg">
 </img>
-<figcaption >
+<figcaption>
-Figure 13. Colony thrives regardless of <i>Varroa</i> mite infestation. Shows <i>Varroa</i> mites in red and worker bee population in blue. Starting population is 10.000 <i>Varroa</i> mites.
+Figure 13. The honey bee population is shown in blue and the <i>Varroa</i> mite population in red. A: Colony rapidly declines when no BeeT is present. Starting population is 20 <i>Varroa</i> B: Colony barely survives <i>Varroa</i> mite infestation. Shows <i>Varroa</i> mite in red and worker bee population in blue. Starting population is 20 <i>Varroa</i>. C: Colony thrives regardless of <i>Varroa</i> mite infestation. Starting population is 20 <i>Varroa</i> mites. D: Colony thrives regardless of heavy <i>Varroa</i> mite infestation. Starting population is 10.000 <i>Varroa</i> mites.
 </figcaption>
 </figure>
-<p>p.s. Remco is finishing the last figures</p>
+<p>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 <i>Lactobacillus</i> species as chassis. This would allow application of BeeT via artificial ‘beebread’.</p>
+<figure>
+<figcaption> Table 1. Three parameter sets per treatment, representing: colony death, survival, and thriving. If colonies can survive and thrive with higher degradation of BeeT (in-hive and outside the hive) and a lower effect of BeeT on <i>Varroa</i> mite mortality, it indicates a more effective treatment.
+</figcaption></figure>
+ <table>
+   		<tr>
+   			<th>
+  			 Period and treatment
+   			</th>
+   			<th>
+  			 Colony death
+   			</th>
+   			<th>
+   			Colony survival
+   			</th>
+			<th>
+			Colony thriving
+			</th>
+ 		</tr>
+  		<tr>
+   			<th>
+  			Sugar water, spring
+   			</th>
+   			<td>
+,6%
+   			</td>
+   			<td>
+,6%
+   			</td>
+			<td>
+,8%
+			</td>
+  		</tr>
+  		<tr>
+    			<th>
+     			Bee bread, spring
+    			</th>
+    			<td>
+%
+    			</td>
+			<td>
+,9%
+			</td>
+			<td>
+,1%
+			</td>
+ 		<tr>
+   			<th>
+   			Sugar water, winter
+   			</th>
+   			<td>
+,1%
+   			</td>
+			<td>
+,7%
+</td>
+  			 <td>
+,2%
+   			</td>
+  </tr>
+<tr>
+   	 		<th>
+     			Bee bread, winter
+    			</th>
+   <td>
+    </td>
+			<td>
+,6%
+			</td>
+			<td>
+,4%
+			</td>
+  </table>
 </section>
+<section id="references">
+<h1>References</h1>
+<ol class="references">
+<a id="res1" href= https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1857359/>1.</a>Bravo, A., Gill, S. S., & Soberon, M. (2007). Mode of action of Bacillus thuringiensis Cry and Cyt toxins and their potential for insect control. Toxicon, 49(4), 423-435.<a href="#refres1" title="Jump back to footnote 1 in the text.">↩</a>
+<br><br>
+<a id="res2" href= http://www.jbc.org/content/284/27/18401.full.pdf>2.</a> Jeff Fabrick, Cris Oppert, Marce´ D. Lorenzen, Kaley Morris, Brenda Oppert, and Juan Luis Jurat-Fuentes. A Novel Tenebrio molitor Cadherin Is a Functional Receptor for Bacillus thuringiensis Cry3Aa Toxin. The Journal of Biological Chemistry VOL. 284, NO. 27, pp. 18401–18410, July 3, 2009.
+<a href="#refres2" title="Jump back to footnote 2 in the text.">↩</a>
+<br><br>
+<a id="res3" href= https://www.ncbi.nlm.nih.gov/pmc/articles/PMC26133/>3.</a> Rajamohan F, Alzate O, Cotrill JA, Curtiss A, Dean DH. Protein engineering of Bacillus thuringiensis δ-endotoxin: Mutations at domain II of CryIAb enhance receptor affinity and toxicity toward gypsy moth larvae. Proceedings of the National Academy of Sciences of the United States of America. 1996;93(25):14338-14343.
+<a href="#refres3" title="Jump back to footnote 3 in the text.">↩</a>
+<br><br>
+<a id="res4" href= http://www.biosecurity.govt.nz/files/pests/varroa/control-of-varroa-guide.pdf>4.</a> Goodwin, M., & Van Eaton, C. (2001). Control of <i>Varroa</i>. A guide for New Zealand Beekeepers. New Zealand Ministry of Agriculture and Forestry (MAF). Wellington, New Zealand.
+<a href="#refres4" title="Jump back to footnote 4 in the text.">↩</a>
+<br><br>
+<a id="res5" href= http://www.nature.com/nature/journal/v518/n7537/abs/nature14121.html >5.</a> Mandell, D. J., Lajoie, M. J., Mee, M. T., Takeuchi, R., Kuznetsov, G., Norville, J. E., ... & Church, G. M. (2015). Biocontainment of genetically modified organisms by synthetic protein design. Nature, 518(7537), 55-60.
+<a href="#refres5" title="Jump back to footnote 5 in the text.">↩</a>
+<br><br>
+</ol>
+</section>
 </html>
 {{Wageningen_UR/footer}}

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%