Difference between revisions of "Team:Wageningen UR/Description/Regulation"
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<h2><b>Subpopulation formation</b></h2> | <h2><b>Subpopulation formation</b></h2> | ||
| − | Using the previously described system, population wide Cry toxin overexpression is likely to kill all <i>E. coli</i> cells in the first wave of toxin expression. It would be beneficial to keep some bacteria alive, as non-producers. These cells would be able to initiate a new growth phase after death of the toxin-producing cells. The critical requirement for this is that some cells can behave differently than the norm of the population, but are genetically identical. A collection of cells that acts differently from the rest of the population is called a <b>subpopulation</b>. | + | Using the previously described system, population-wide Cry toxin overexpression is likely to kill all <i>E. coli</i> cells in the first wave of toxin expression. It would be beneficial to keep some bacteria alive, as non-producers. These cells would be able to initiate a new growth phase after death of the toxin-producing cells. The critical requirement for this is that some cells can behave differently than the norm of the population, but are genetically identical. A collection of cells that acts differently from the rest of the population is called a <b>subpopulation</b>. |
<br> | <br> | ||
<br> | <br> | ||
| − | For bacteria to produce a subpopulation there must exist two states in which an individual cell can exist. We based our states on the dominance of one of two proteins over the other. The working of the dominantly present protein determines cell behaviour. The proteins that we chose to include in the system (figure 3) are phage λ- and phage 434 cI repressor protein. <a href="http://parts.igem.org/Part:BBa_I12006">A promoter</a> has been modified previously that is regulated by the two cI proteins in opposite ways: the λ cI repressor protein induces the promoter, whereas the cI repressor protein from phage 434 represses the promoter. To create a subpopulation, the levels of the two cI proteins (or at least the ratios between them) should vary between cells. Inspired by persister cell formation, we investigated expressing the two cI genes in one operon behind the same promoter. Differences in the turnover of the two proteins causes dynamics: changes in promoter strength induce temporary changes in the ratio between the level of the proteins. Imagine a system where both cI genes are transcribed at intermediate levels. 434 cI has a much stronger ribosome binding site (RBS) than the λ cI gene and therefore is translated at a higher rate. This leads to a situation where there is more 434 cI than λ cI: 434 cI is dominant. When the promoter regulating both genes is repressed, both genes will in time reach a new (lower) balance of protein levels. However, as 434 cI is degraded faster than λ cI, 434 cI protein levels will decrease more rapidly. This allows for brief domination of λ cI. We hypothesize that small differences (cell age, metabolism etc.) between cells can determine whether λ cI indeed gets the chance to dominate the system. | + | For bacteria to produce a subpopulation there must exist two states in which an individual cell can exist. We based our states on the dominance of one of two proteins over the other. The working of the dominantly present protein determines cell behaviour. The proteins that we chose to include in the system (figure 3) are phage λ- and phage 434 cI repressor protein. <a href="http://parts.igem.org/Part:BBa_I12006">A promoter</a> has been modified previously that is regulated by the two cI proteins in opposite ways: the λ cI repressor protein induces the promoter, whereas the cI repressor protein from phage 434 represses the promoter. To create a subpopulation, the levels of the two cI proteins (or at least the ratios between them) should vary between cells. Inspired by persister cell formation, we investigated expressing the two cI genes in one operon behind the same promoter. Differences in the turnover of the two proteins causes dynamics: changes in promoter strength induce temporary changes in the ratio between the level of the proteins. Imagine a system where both cI genes are transcribed at intermediate levels. 434 cI has a much stronger ribosome binding site (RBS) than the λ cI gene and therefore is translated at a higher rate. This leads to a situation where there is more 434 cI than λ cI: 434 cI is dominant. When the promoter regulating both genes is repressed, both genes will in time reach a new (lower) balance of protein levels. However, as 434 cI is degraded faster than λ cI, 434 cI protein levels will decrease more rapidly. This allows for brief domination of λ cI. We hypothesize that small differences (cell age, metabolism etc.) between cells can determine whether λ cI indeed gets the chance to dominate the system. |
| + | <br><br> | ||
| + | The principle described here is similar to (and was inspired by) how some cells become dormant persister cells, while others continue growing<sup><a href="#pd5" id="refpd5">5</a></sup>. Persisters cells are a well studied yet poorly understood example of microbial subpopulations<sup><a href="#pd6" id="refpd6">6</a></sup>. Persister cells make up around 1% of the population in the stationary phase<sup><a href="#pd7" id="refpd7">7</a></sup>. In the main model for persister cells, persisters differ from other cells in the balance between toxin and antitoxin. In these cells, the toxin dominates the antitoxin. This causes dormancy, allowing persisters to escape antibiotics and other environmental factors that might kill growing cells<sup><a href="#pd6" id="refpd6">6</a></sup>. <b> We decided against an attempt to create actual persisters out of safety reasons.</b> Persister cell formation can be expected to increase the chance of bacteria evading kill-switch mechanisms and other bio-containment measures<sup><a href="#pd9" id="refpd9">9</a></sup>. | ||
<br><br> | <br><br> | ||
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We hypothesized that the system can only work when the balance between the cI proteins is in a certain ‘sweet spot’ of ratios. To improve our chances of hitting this sweet spot we incorporated a library of 18 possible RBS’s upstream of the <i>434 cI</i> gene. The library was kindly designed by Daniel Gerngross using the Redlibs algorithm<sup><a href="#pd8" id="refpd8">8</a></sup>. This provided us with a library of limited size representing a linear increase in predicted translation rates. This way we varied the translation rates of 434 cI between clones, resulting in different ratios between the λ and 434 cI protein levels for these clones. | We hypothesized that the system can only work when the balance between the cI proteins is in a certain ‘sweet spot’ of ratios. To improve our chances of hitting this sweet spot we incorporated a library of 18 possible RBS’s upstream of the <i>434 cI</i> gene. The library was kindly designed by Daniel Gerngross using the Redlibs algorithm<sup><a href="#pd8" id="refpd8">8</a></sup>. This provided us with a library of limited size representing a linear increase in predicted translation rates. This way we varied the translation rates of 434 cI between clones, resulting in different ratios between the λ and 434 cI protein levels for these clones. | ||
| − | + | <br> | |
DH5α cells transformed with the plasmid - containing both operons and the RBS library - yielded clones with varying intensities of red coloring on agar (without both L-Arabinose and D-Glucose) plates (Figure 4). This diversity is a first indication that the assembled system works as intended: variation in the 434 cI RBS should cause different ratios between λ and 434 cI protein levels, leading to differences in mRFP expression. | DH5α cells transformed with the plasmid - containing both operons and the RBS library - yielded clones with varying intensities of red coloring on agar (without both L-Arabinose and D-Glucose) plates (Figure 4). This diversity is a first indication that the assembled system works as intended: variation in the 434 cI RBS should cause different ratios between λ and 434 cI protein levels, leading to differences in mRFP expression. | ||
<figure> | <figure> | ||
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<br> | <br> | ||
We chose to further analyze a selection of the colonies that either showed a unique response or represented a general response found in many colonies. For these colonies we compared the development of mRFP activity between samples grown in only LB with the selection antibiotic, on the same medium with L-Arabinose and on medium with L-Arabinose and Glucose. | We chose to further analyze a selection of the colonies that either showed a unique response or represented a general response found in many colonies. For these colonies we compared the development of mRFP activity between samples grown in only LB with the selection antibiotic, on the same medium with L-Arabinose and on medium with L-Arabinose and Glucose. | ||
| − | + | <br> | |
According to our expectation, glucose repression should lead to a subset of cells starting to produce substantial levels of mRFP. Therefore, addition of glucose should increase the total mRFP activity in the population. This is not clearly found in our results (link to results in the notebook), but the development of mRFP activity in colonies B6, C10, E11 and H9 preliminarily suggests an increase in fluorescence in samples with Glucose starting after approximately 11 hours relative to the samples lacking Glucose. | According to our expectation, glucose repression should lead to a subset of cells starting to produce substantial levels of mRFP. Therefore, addition of glucose should increase the total mRFP activity in the population. This is not clearly found in our results (link to results in the notebook), but the development of mRFP activity in colonies B6, C10, E11 and H9 preliminarily suggests an increase in fluorescence in samples with Glucose starting after approximately 11 hours relative to the samples lacking Glucose. | ||
<br><br> | <br><br> | ||
| − | Up to now, we only described experiments that measure the | + | Up to now, we only described experiments that measure the populationwide response in mRFP activity. Of course, subpopulations could only really be observed when fluorescence is assessed for individual cells. To this end we performed fluorescence microscopy on colonies B6, C10, E11 and H9, comparing samples grown in LB (with selection antibiotics) with L-Arabinose to samples where we also added D-Glucose. In an effort to visualize the number of cells that show fluorescence among the other cells, we chose to make pictures where the cells were exposed to mRFP excitation with laser light, but also to low levels of white-light (link to results in the notebook). |
| − | + | <br><br> | |
In an effort to quantify the effect glucose has on the formation of subpopulations we performed a flow cytometry experiment. This analysis was done for two of the clones: B6 and C10. This method clearly revealed that glucose addition has little or no effect on the formation of subpopulations in these cells. | In an effort to quantify the effect glucose has on the formation of subpopulations we performed a flow cytometry experiment. This analysis was done for two of the clones: B6 and C10. This method clearly revealed that glucose addition has little or no effect on the formation of subpopulations in these cells. | ||
Constitutive mRFP expressors - used as positive control - show a high mean fluorescence. The cells containing the subpopulation system have a lower, but substantial mean fluorescence per cell. At first glance it seem that the subpopulation system causes a more diverse population in mRFP expression, but this could well be an artifact because of the logarithmic scale on the x-axis (count/ fluorescence figures). | Constitutive mRFP expressors - used as positive control - show a high mean fluorescence. The cells containing the subpopulation system have a lower, but substantial mean fluorescence per cell. At first glance it seem that the subpopulation system causes a more diverse population in mRFP expression, but this could well be an artifact because of the logarithmic scale on the x-axis (count/ fluorescence figures). | ||
<br> | <br> | ||
Although flow cytometry shows that two clones do not form subpopulations, this is no reason to assume that none of the clones are able to do so. We chose to include an 18-mer RBS library on purpose. We expect that if the system described here can lead to subpopulations, this ability relies on the RBS strength. As we have not been able to measure all RBS inserts, we cannot accept nor reject the possibility that the system described here works as intended. | Although flow cytometry shows that two clones do not form subpopulations, this is no reason to assume that none of the clones are able to do so. We chose to include an 18-mer RBS library on purpose. We expect that if the system described here can lead to subpopulations, this ability relies on the RBS strength. As we have not been able to measure all RBS inserts, we cannot accept nor reject the possibility that the system described here works as intended. | ||
| − | < | + | <h2><b>Conclusion</b></h2> |
The two systems, quorum sensing, and subpopulation formation, were designed to work in conjunction (figure 9). When high amounts of the luxR-AHL complex are present, those would repress the 434 and λ cI operon instead of Glucose. This requires the promoter of the 434 and λ cI operon to be changed to the BBa_R0063 promoter. We were unable to construct and test this total combined system due to time constraints. However, in an attempt to predict the population dynamics that result from such a system, Angelina modelled first the separate systems and then combined her models. To read about the modelling of these systems click here: link. | The two systems, quorum sensing, and subpopulation formation, were designed to work in conjunction (figure 9). When high amounts of the luxR-AHL complex are present, those would repress the 434 and λ cI operon instead of Glucose. This requires the promoter of the 434 and λ cI operon to be changed to the BBa_R0063 promoter. We were unable to construct and test this total combined system due to time constraints. However, in an attempt to predict the population dynamics that result from such a system, Angelina modelled first the separate systems and then combined her models. To read about the modelling of these systems click here: link. | ||
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</figure><br/> | </figure><br/> | ||
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We show the working of quorum sensing with a newly constructed <a href="http://parts.igem.org/Part:BBa_K1913014">reporter</a>. Quorum sensing can be used to change the expression pattern of Cry toxins in time to minimize negative effects of Cry toxin toxicity to <i>E. coli</i>. | We show the working of quorum sensing with a newly constructed <a href="http://parts.igem.org/Part:BBa_K1913014">reporter</a>. Quorum sensing can be used to change the expression pattern of Cry toxins in time to minimize negative effects of Cry toxin toxicity to <i>E. coli</i>. | ||
| − | In addition we propose a system for subpopulation formation to further decrease effects of Cry toxin toxicity on BeeT. Despite our efforts to test the system we cannot draw a definite conclusion on whether the system indeed is able to induce subpopulation formation. | + | In addition, we propose a system for subpopulation formation to further decrease effects of Cry toxin toxicity on BeeT. Despite our efforts to test the system, we cannot draw a definite conclusion on whether the system indeed is able to induce subpopulation formation. |
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Revision as of 08:58, 12 October 2016
Regulation of toxin expression
A problem that is encountered when expressing Bt toxins is that overexpression can be lethal to the bacterial chassis, in our case Escherichia coli. We aimed to prevent premature lysis of BeeT and achieve higher toxin yield by separating the growth phase from the toxin-producing phase. Toxin expression is coupled to quorum-sensing: only when a sufficient number of bacteria are present, the toxin is produced. Furthermore, we aimed to create a subpopulation of bacteria that do not produce the toxin, even when the bacterial density is high. When the toxin-producing bacteria perish, the subpopulation survives. As the survivors are genetically identical to the rest of the population, they are able to initiate a new growth phase and subsequently new toxin production.
The high toxin expression is needed to induce significant damage to the Varroa population. It is known that longer-term, low-dose exposure of a pesticide to the target organism can cause resistance (Tabashnik, Brévault & Carrière, 2013). Ideally, the toxin is only expressed when the target organism is present. To further optimize toxin expression, two measures to regulate expression were explored. BeeT was designed to sense the presence of Varroa destructor through the use of two riboswitches: one senses guanine, the other senses vitamin B12. Both are normally not present in beehives, but indicate the presence of the mite as guanine is a major component of mite faeces and B12 is present in the haemolymph of the bees. When a mite attaches itself to a bee to feed on the haemolymph, BeeT will be able to sense it.
Lastly, a toggle switch for toxin expression was designed, by combining the riboswitch and part of the light killswitch employed in our safety system (hyperlink). A hybrid promoter was designed that both facilitates continuous toxin production after transient sensing of the mite, and shuts off production when BeeT escapes from the hive and is exposed to light.
Population Dynamics
Altering Population Dynamics to Improve Toxin Production
In our quest to save the honey bees from Varroa destructor we envisioned using Bacillus thuringiensis Cry toxins. When activated and concentrated near the surface of cell membranes, Cry toxins form pores inside the membrane, lysing the cell as a result1. High level constitutive expression of such toxins in Escherichia coli is known to inhibit growth and possibly kill the producing cells2. The aim behind the population dynamics subproject is to provide a system for toxin regulation where production does not impair growth nor population survival of the bacteria.Solutions to the problem of toxic expression are found in mechanisms that regulate protein production to minimize the negative effects on growth and survival3. Inducible expression for instance, is widely used to manually delay toxin expression until after the exponential growth phase of the bacteria. We cannot expect beekeepers to measure bacterial growth and induce expression themselves when the time is right. We propose a regulatory system that enables E. coli to separate the growth and the production phase, only expressing recombinant proteins when bacterial cell density is high.
Quorum Sensing: Bacterial Density Based Regulation
Systems that enable bacteria to regulate expression based on their density are called quorum sensing mechanisms. We have adopted one of the best known quorum sensing systems: the lux system originating form Vibrio fischeri. We demonstrated this system using a newly constructed GFP reporter . The lux system consists of luxI and luxR; two genes that allow bacteria to communicate their density. LUXI is a synthase that produces acyl-homoserine lactones (AHLs). AHLs are small compound that diffuse across cell-membranes. They function as autoinducers; molecules that bacteria secrete to signal population density. A single cell produces insufficient autoinducer molecules to start quorum sensing. However, when there is a high density of bacteria that produce AHL, the AHL concentration in the medium increases. Eventually, the cytoplasmic concentration of AHL is high enough to effectively bind LUXR. LUXR is a cytoplasmic receptor protein that binds AHLs. LUXR then further induces expression of luxI. This positive feedback is the crucial component of quorum sensing.To test density dependent expression, two strains of E. coli DH5α were constructed. The first strain contains two plasmids: one with the actual quorum sensing system and one with the GFP quorum sensing reporter(figure 1). Either strain should be able to communicate their cell density and produce GFP when cell density is high enough. The bacteria were indeed found to acquire green color, preliminarily indicating functional quorum sensing and reporting. The cells that contain only the reporter plasmid did not turn green, indicating that they are not able to communicate their cell density.
To investigate the dynamics of the two quorum sensing strains, plate reader measurements were used. We measured GFP fluorescence and optical density to correct total GFP production for the amount of cells present (figure 2). There is a clear difference between the responses of the quorum sensing strains and the negative control that only contains the GFP reporter plasmid. Fluorescence in the negative control barely rises above the x-axis. However, both quorum sensing strains show a steep response in GFP expression at comparable optical densities. This indicates that both tested systems function as was envisioned and provide a means of regulating gene expression based on bacterial density. In principle, this allows for Cry toxin expression if, and only if, bacterial density is sufficient.
Subpopulation formation
Using the previously described system, population-wide Cry toxin overexpression is likely to kill all E. coli cells in the first wave of toxin expression. It would be beneficial to keep some bacteria alive, as non-producers. These cells would be able to initiate a new growth phase after death of the toxin-producing cells. The critical requirement for this is that some cells can behave differently than the norm of the population, but are genetically identical. A collection of cells that acts differently from the rest of the population is called a subpopulation.For bacteria to produce a subpopulation there must exist two states in which an individual cell can exist. We based our states on the dominance of one of two proteins over the other. The working of the dominantly present protein determines cell behaviour. The proteins that we chose to include in the system (figure 3) are phage λ- and phage 434 cI repressor protein. A promoter has been modified previously that is regulated by the two cI proteins in opposite ways: the λ cI repressor protein induces the promoter, whereas the cI repressor protein from phage 434 represses the promoter. To create a subpopulation, the levels of the two cI proteins (or at least the ratios between them) should vary between cells. Inspired by persister cell formation, we investigated expressing the two cI genes in one operon behind the same promoter. Differences in the turnover of the two proteins causes dynamics: changes in promoter strength induce temporary changes in the ratio between the level of the proteins. Imagine a system where both cI genes are transcribed at intermediate levels. 434 cI has a much stronger ribosome binding site (RBS) than the λ cI gene and therefore is translated at a higher rate. This leads to a situation where there is more 434 cI than λ cI: 434 cI is dominant. When the promoter regulating both genes is repressed, both genes will in time reach a new (lower) balance of protein levels. However, as 434 cI is degraded faster than λ cI, 434 cI protein levels will decrease more rapidly. This allows for brief domination of λ cI. We hypothesize that small differences (cell age, metabolism etc.) between cells can determine whether λ cI indeed gets the chance to dominate the system.
The principle described here is similar to (and was inspired by) how some cells become dormant persister cells, while others continue growing5. Persisters cells are a well studied yet poorly understood example of microbial subpopulations6. Persister cells make up around 1% of the population in the stationary phase7. In the main model for persister cells, persisters differ from other cells in the balance between toxin and antitoxin. In these cells, the toxin dominates the antitoxin. This causes dormancy, allowing persisters to escape antibiotics and other environmental factors that might kill growing cells6. We decided against an attempt to create actual persisters out of safety reasons. Persister cell formation can be expected to increase the chance of bacteria evading kill-switch mechanisms and other bio-containment measures9.
To test the viability of the described subpopulation system, we constructed a plasmid where λ and 434 cI encoding genes are under control of the pBAD/AraC promoter. This promoter is can be induced with L-Arabinose and repressible with D-Glucose. This control over expression is useful to manipulate the formation of the subpopulations as envisioned in our design. Another operon was constructed with the modified λ Prm promoter controlling mRFP expression, representing expression of the Cry toxin. Additional design notes (should be collapsable) The 434 cI repressor protein encoded on the plasmids contains a C-terminal LVA tag, increasing the protein’s degradation rate. The modified λ Prm promoter also controls an additional λ cI gene that provides positive feedback on the λ Prm promoter. This additional λ cI should be degraded more rapidly than the λ cI gene in the inducible operon as it is LVA-tagged. We hypothesized that the system can only work when the balance between the cI proteins is in a certain ‘sweet spot’ of ratios. To improve our chances of hitting this sweet spot we incorporated a library of 18 possible RBS’s upstream of the 434 cI gene. The library was kindly designed by Daniel Gerngross using the Redlibs algorithm8. This provided us with a library of limited size representing a linear increase in predicted translation rates. This way we varied the translation rates of 434 cI between clones, resulting in different ratios between the λ and 434 cI protein levels for these clones.
DH5α cells transformed with the plasmid - containing both operons and the RBS library - yielded clones with varying intensities of red coloring on agar (without both L-Arabinose and D-Glucose) plates (Figure 4). This diversity is a first indication that the assembled system works as intended: variation in the 434 cI RBS should cause different ratios between λ and 434 cI protein levels, leading to differences in mRFP expression.
To provide a more quantitative measurement of the mRFP production and to see how this develops during bacterial growth, we analyzed 95 colonies (from the RBS library transformation) in an overnight plate reader experiment. The data from this experiment (link to results in the notebook) show that the majority of the colonies display roughly the same pattern of mRFP production during growth after Glucose addition. Roughly 4 hours after Glucose addition an increase in fluorescence/OD600 is seen for almost all colonies.
We chose to further analyze a selection of the colonies that either showed a unique response or represented a general response found in many colonies. For these colonies we compared the development of mRFP activity between samples grown in only LB with the selection antibiotic, on the same medium with L-Arabinose and on medium with L-Arabinose and Glucose.
According to our expectation, glucose repression should lead to a subset of cells starting to produce substantial levels of mRFP. Therefore, addition of glucose should increase the total mRFP activity in the population. This is not clearly found in our results (link to results in the notebook), but the development of mRFP activity in colonies B6, C10, E11 and H9 preliminarily suggests an increase in fluorescence in samples with Glucose starting after approximately 11 hours relative to the samples lacking Glucose.
Up to now, we only described experiments that measure the populationwide response in mRFP activity. Of course, subpopulations could only really be observed when fluorescence is assessed for individual cells. To this end we performed fluorescence microscopy on colonies B6, C10, E11 and H9, comparing samples grown in LB (with selection antibiotics) with L-Arabinose to samples where we also added D-Glucose. In an effort to visualize the number of cells that show fluorescence among the other cells, we chose to make pictures where the cells were exposed to mRFP excitation with laser light, but also to low levels of white-light (link to results in the notebook).
In an effort to quantify the effect glucose has on the formation of subpopulations we performed a flow cytometry experiment. This analysis was done for two of the clones: B6 and C10. This method clearly revealed that glucose addition has little or no effect on the formation of subpopulations in these cells. Constitutive mRFP expressors - used as positive control - show a high mean fluorescence. The cells containing the subpopulation system have a lower, but substantial mean fluorescence per cell. At first glance it seem that the subpopulation system causes a more diverse population in mRFP expression, but this could well be an artifact because of the logarithmic scale on the x-axis (count/ fluorescence figures).
Although flow cytometry shows that two clones do not form subpopulations, this is no reason to assume that none of the clones are able to do so. We chose to include an 18-mer RBS library on purpose. We expect that if the system described here can lead to subpopulations, this ability relies on the RBS strength. As we have not been able to measure all RBS inserts, we cannot accept nor reject the possibility that the system described here works as intended.
Conclusion
The two systems, quorum sensing, and subpopulation formation, were designed to work in conjunction (figure 9). When high amounts of the luxR-AHL complex are present, those would repress the 434 and λ cI operon instead of Glucose. This requires the promoter of the 434 and λ cI operon to be changed to the BBa_R0063 promoter. We were unable to construct and test this total combined system due to time constraints. However, in an attempt to predict the population dynamics that result from such a system, Angelina modelled first the separate systems and then combined her models. To read about the modelling of these systems click here: link.
We show the working of quorum sensing with a newly constructed reporter. Quorum sensing can be used to change the expression pattern of Cry toxins in time to minimize negative effects of Cry toxin toxicity to E. coli. In addition, we propose a system for subpopulation formation to further decrease effects of Cry toxin toxicity on BeeT. Despite our efforts to test the system, we cannot draw a definite conclusion on whether the system indeed is able to induce subpopulation formation. � �
References
1. Bravo, A., Gómez, I., Porta, H., García-Gómez, B. I., Rodriguez-Almazan, C., Pardo, L., & Soberón, M. (2013). Evolution of Bacillus thuringiensis Cry toxins insecticidal activity. Microbial Biotechnology, 6(1), 17–26. ↩2. Douek, J., Einav, M., & Zaritsky, A. (1992). Sensitivity to plating of Escherichia coli cells expressing the cryA gene from Bacillus thuringiensis var. israelensis. MGG Molecular & General Genetics, 232(1), 162–165. ↩
3. Saida, F., Uzan, M., Odaert, B., & Bontems, F. (2006). Expression of Highly Toxic Genes in E. coli: Special Strategies and Genetic Tools. Current Protein and Peptide Science, 7(1), 47–56. ↩
4. Pearson, B., Lau, K. H., DeLoache, W., Penumetcha, P., Rinker, V. G., Allen, A., … Campbell, A. M. (2011). Bacterial Hash Function Using DNA-Based XOR Logic Reveals Unexpected Behavior of the LuxR Promoter. Interdisciplinary Bio Central, 3(3), 1–8. ↩
5. Van Melderen, L., & Saavedra De Bast, M. (2009). Bacterial Toxin–Antitoxin Systems: More Than Selfish Entities? PLoS Genetics, 5(3), e1000437. ↩
6. Wood, T. K., Knabel, S. J., & Kwan, B. W. (2013). Bacterial persister cell formation and dormancy. Applied and Environmental Microbiology, 79(23), 7116–21. ↩
7. Lewis, K. (2007). Persister cells, dormancy and infectious disease. Nature Reviews Microbiology, 5(1), 48–56. ↩
8. Jeschek, M., Gerngross, D., Panke, S., Canton, B., Labno, A., Endy, D., Perezmartin, J. (2016). Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort. Nature Communications, 7, 11163. ↩
9. Engelberg-Kulka, H., Amitai, S., Kolodkin-Gal, I., & Hazan, R. (2006). Bacterial Programmed Cell Death and Multicellular Behavior in Bacteria. PLoS Genetics, 2(10), e135. ↩
Design of a mite sensing system based on riboswitches
Design of a mite sensing system based on riboswitches
To prevent toxin expression when the mite is not present, we have created a mite sensing system that can regulate toxin expression using riboswitches. Substances that indicate the presence of the mite are guanine, since 95% of the mite faeces consist of guanine1 and vitamin b12 since this vitamin is present in the haemolymph of insects. V. destructor feeds on the haemolymph of the honey bee and will leave traces of haemolymph in encapsulated larvae cells and through the hive1.
Guanine and vitamin b12 sensitive riboswitches were used to design the system. Riboswitches are pieces of mRNA that can regulate gene expression depending on if it is bound to a ligand. E. coli possesses a vitamin b12 riboswitch that regulates the expression of the btuB genes3. It also possesses b12 receptors on its outer membrane, allowing vitamin b12 uptake4. The vitamin b12 btuB riboswitch will be used to design a system that will start gene expression in the presence of vitamin b12. For the guanine riboswitch the xpt-buX operon of Bacillus subtilis has been used. E.coli possesses guanine receptors6, so no additional transporters are needed.
The riboswitches regulate translation of genes at the mRNA level2, so they need to be constitutively transcribed. Therefore, a promoter of the Anderson constitutive promoter family will be placed in front of the riboswitch. It is the consensus constitutive promoter family of iGEM, it is well documented and the amount of expression differs 2500 fold between different promoters. It can also be easily swapped for another promoter of the same family if a different amount of expression is needed7. This is convenient since the optimal expression level of the toxin gene is not known yet.
Riboswitches are known to be able to both start and stop gene expression upon binding to specific ligands. Both the guanine and vitamin b12 riboswitch will stop expression after binding to the ligands4,5. In order for the system to work, a component needs to be added that will invert this regulation. For this the TetR Quad-part inverter system has been used. Quad-part inverters, or QPI, are genetic regulatory inverters that consists of a ribosome binding site, a coding region for a repressor protein, a terminator and a promoter that is regulated by the encoded repressor protein8. The TetR system will be used since it is widely used and characterized as a well-functioning inverter8. Figure 1 shows a schematic overview of the system with the riboswitch, TetR QPI and monomeric Red Fluorescent Protein or mRFP.
An often encountered problem when creating a novel combination of genetic elements is the fact that even seemingly simple genetic functions behave differently in different settings9. Therefore, the behaviour of the designed construct might be different than expected. To make the system more stable or increase the chance that the system will behave as designed, the riboswitch system will be made bicistronic. This means that two genes are under the control of one promoter. In this case, the sequence of the riboswitches has been cloned together with fifty amino acids downstream of the riboswitch in the original genomic sequence of B. Subtilis and E. coli. Adding fifty amino acids to the system means an increase of protein production for the cell which can be stressful. Therefore a ssRA protein degradation tag has been added behind the additional fifty amino acids. The protein part in front of this tag will be degraded after translation. For the design of this system, the E. coli ssRA tag10, that consists of around thirty base pairs, has been used.
Cloning the mite sensing system based on riboswitches
The cloning of the system has been done with the use of a 3A-assembly out of two constructs. The first part is called B/Gribo and contains the riboswitch and constitutive promoter. The second part is the inverter part, consisting out of the TetR QPI and mRFP as a reporter gene.
Creating riboswitch part with PCR and special primers
A part containing the riboswitch has been created with the use of special primers that copies the correct riboswitch from genomic DNA. During the PCR, the primer has added the constitutive promoter and ssRA-tag. This part has been dubbed B-ribo and G-ribo (of vitamin b12 and guanine riboswitch). The expected length of B-ribo is 537 basepairs (Figure X, red arrow) and of G-ribo 468 basepairs (Figure X, red arrow)
Creating the inverter part out of bio bricks
The other part of the construct is the inverter-part, containing the TetR gene, a promoter that is inhibited by TetR and the reporter gene mRFP. This part is called the Inv-part. The Inv-part has been created out of standard part BBa_p0140 and BBa_I13521 from the iGEM kit as can be seen in figure X.
The PCR of the Inv-part shows a band between 2000 and 3000 basepairs where a band of 2122 base pairs is expected, as can be seen in figure X.
Combing the riboswitch part and the inverter part to clone the designed construct
Out of the ribo-part and the Inv-part, the designed system B/GRInv has been assembled via a 3a-assembly. The transformation of B/GRinv out of B/Gribo and Inv-part in psB1C3 has resulted in three different types of colonies: white, pink and red ones.
If no guanine or vitamin b12 are present, a colony containing the R/GRInv construct should have a white colour since TetR is expressed when no metabolite is present to bind to the riboswitch. TetR in its turn, will inhibit the promoter that normally expresses mRFP. However, in LB agar, small amounts of vitamin and guanine are present and therefore it is not possible to be sure which colony is the right one based on its colour: white, pink or red. PCR results show that only the pink colonies have the expected size of the B/GRInv construct as can be seen in figure X. Sequencing results confirm that the pink colonies contain the right construct.
Toggle Switch
Overview:
Although quorum sensing system could be a good strategy to keep BeeT’s toxin expression in a reasonable range, it is difficult to apply this system into realistic beehive conditions since BeeT may not growth to a certain density in the brood cells Therefore, we decided to engineer a regulatory system more suitable for the beehive context in beehive as a parallel strategy. In order to integrate mite sensing and light-control for regulating BeeT’s toxin expression, we designed a toggle switch. It consists of two repressors and allows controlling BeeT’s toxin expression under two different stimulus, guanine (or vitamin B12) and blue light. This toggle switch is based on a well-known genetic toggle switch developed by Gardner et al (Gardner T et al., 2000). On it the mutual repression of two repressor proteins results in bi-stability of the system, which could be switched between two stable steady states rapidly. These steady states are off-state and on-state. In our toggle switch, the on-state leads to the stable expression of BeeT’s toxin and it is only reached when the system detects that both BeeT is in the beehive (dark) and mites are present in the brood cells (guanine or vitamin B12). In conclusion, we constructed a regulatory system that could be applied into real beehive condition theoretically and it integrates mite sensing with light-control device, which could provide a stable output of toxin expression with fast respon-siveness.
How does light regulate BeeT?
In this part, we show the design of a whole regulatory system that connects the toggle switch and the light kill switch by means of light (Fig.1). The toggle switch controls expression of the BeeT’s toxin between an off-state and an on-state, which can be switched with two stimulus, guanine (or vitamin B12) and blue light. Our mite sensor is based on two types of riboswitches, one causes transcription termination and the other prevents transcription initiation by locating on the 5’ UTR region of the mRNA attenuating gene translation after binding a metabolite (Mandal, M., & Breaker, R. R., 2004).
The light sensor makes use of YF1 and FixJ. YF1 is the fusion of the LOV protein with a histidine kinase (Möglich A et al., 2009). In the absence of light, YF1 can activate FixJ by phosphorylation resulting in the activation of the Fixk2 promoter. In the presence of 480 nm wavelength light, YF1 can no longer phosphorylate FixJ due to the change position of a salt bridge on the LOV domain, leading to the deactivation of the Fixk2 promoter (Crosson S et al., 2003).
Changes on the guanine or vitamin B12 and light conditions result in different situations on the bee hive.
Situation 1:
Initially, in the brood cells, the expression of the BeeT’s toxin is inhibited by the LacI repressor since the phosphorylation process between YF1 and FixJ takes some time to activate Fixk2 and produce enough TetR repressor to inhibit ptet (promoter driving the expression of LacI). On this situation the system remains in the off-state (Fig.1 a). At the same time, MazE is constitutively expressed from the light Kill Switch preventing the death of the cells.
Situation 2:
When mites are present in the brood cells, they produce and release guanine and vitamin B12 into the microenvironment of the brood cells. The binding of these metabolites to the riboswitches on the 5’ UTR of the LacI mRNA will attenuate translation of LacI (Fig.1 b) resulting on the stop of the inhibition of the FixK2-plac hybrid promoter and initiating the expression of TetR and BeeT’s toxin (Fig.1 c). At this point the system has switched to on-state and any small perturbations will not influence the stable expression of BeeT’s toxin. As cells are still in dark, MazE continues gaining over MazF and cells are alive.
Situation 3:
Once our BeeT escapes from the brood cell, the exposition to light would inhibit the phosphorylation process between YF1 and FixJ, resulting in the inactivation of FixJ and stopping the transcription from the hybrid promotor, FixK2 promoter on Kill Switch. TetR can no longer inhibit the ptet promotor and the system switch back to the off-state (Fig.1 d). In the meantime, MazF would start being expressed, which leads to programmed cell death reducing the the environmental impact of BeeT.
Hybrid promoter design:
In order to make Fixk2 promoter as an inducible promoter for the toggle switch, which could also be compatible with the light kill switch, we had to add additional repressor operator sequences into the Fixk2 sequence. Hence, the whole structure of Fixk2 should be figured out. However, we could not find any literature demonstrating the definite structure of the Fixk2 promoter (BBa_K592006) from the iGEM parts registry and even the original Fixk2 promoter from pDawn and pDusk system (Ohlendorf R et al., 2012) does not have a detailed structural analysis. So we decided to construct five different FixK2 hybrid promoters (BBa_K1913022, BBa_K1913023, BBa_K1913024, BBa_K1913025, BBa_K1913026) based on the sequence of the wild type FixK2 promoter of Bradyrhizobium japonicum (Nellen-Anthamatten D et al., 1998). Nellen-Anthamatten D et al made a definite sequence structure analysis of this Fixk2, showing the presence of two FixJ boxes between the -40 and -70 region and a -10 to -35 core element (Fig. 2, a). According to this sequence structure, we added two additional LacI operators, both upstream and downstream the Fixk2 sequence (Fig 2, b), which could be bonded by tetrameric Lac repressor resulting in the formation of a DNA loop and consequently on transcription repression (Oehler S et al., 1990). We also designed a ptet-Fixk2 hybrid promoter by inserting two TetR operators into the core element region so that Tet repressors could form a dimmer and bond to these operators, resulting in transcription repression (Lutz R et al., 1997). However, according to some previous iGEM projects (UNITN-Trento 2013, INSA-Toulouse 2013), the transcription activity of the wild type Fixk2 promoter is so weak that they all added an inverter part to control their target gene expression. Even the original pDusk system in darkness has only 5 times expression levels than in light conditions. Therefore, we decided to enhance the transcriptional activity of the Fixk2 promoter by changing the core element region of the wild type Fixk2 by a strong constitutive promoter (BBa_J23106) from iGEM part registry and by adding two typical FixJ boxes (Mesa S et al., 2005; Ferrières L et al., 2002) into the -40 to -70 region (Fig.3 a, b) In addition, we designed an additional plac-Fixk2 as a backup (Fig.3, d) via changing core element into -10 to -35 region of ompC promoter from another two component system (Mizuno T et al., 1986), because we couldn’t guarantee that FixJ boxes could be compatible with the core element of a constitutive promoter.
Hybrid promoters are activated in the dark in presence of YF1-FixJ
In order to test the hybrid promoters, we constructed five composite parts with mRFP gene as reporter (BBa_K19103027, BBa_K19103028, BBa_K19103029, BBa_K19103030, BBa_K19103031). These composite parts were co-transformed with the light sensor part (BBa_K19103034) into E.coli strain BL21, cultured under dark for 24h and their fluorescence was tested. We included as controls cells that only contained the Fixk2 composite parts. The results (Fig.4) illustrated that hybrid promoter BBa_K1913022 has 5 times significant different compared to control, which suggested that this promoter is the most sensitive one for being induces by FixJ. Hybrid promotores BBa_K1913023 and BBa_K1913024 showed relative higher florescence values than others, suggesting that they have certain leaky expression. Whereas there were no signifi-cant difference between these promotores and their controls, this means that these two pro-moters are very less sensitive to induction by FixJ than the others.
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