Difference between revisions of "Template:NYMU-2015project-wetlab"

 
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<h2>Introduction</h2>
 
<h2>Introduction</h2>
 
<img src="https://static.igem.org/mediawiki/parts/9/95/Elecmtrb.jpg" style="padding-right:13%;padding-bottom:4%;padding-left:4%; width: 30%;float:right;">
 
<img src="https://static.igem.org/mediawiki/parts/9/95/Elecmtrb.jpg" style="padding-right:13%;padding-bottom:4%;padding-left:4%; width: 30%;float:right;">
<p>  One major problem in controlling potato late blight is that there is no simple and convenient way to detect the disease. If we want to make sure that our potato has not been infected by <i>P. infestans</i> , we have to examine the potato in the laboratory for approximately a week. We want our detection system to be simple and convenient, and can instantly report the infection to the user. So the solution we reached is to design a soil based microbial fuel cell (SMFC) that can detect salicylic acid, a chemical that is produced when potato is injured or infected. With this device, we can know whether the potato is infected immediately at home.</p>
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<p>  One major problem in controlling potato late blight is that there is no simple and convenient way to detect the disease. If we want to make sure that our potato has not been infected by <i>P. infestans</i> , we have to examine the potato in the laboratory for approximately a week. We want our detection system to efficient and can instantly report the infection to the user. So the solution we reached is to design a soil based microbial fuel cell (SMFC) that can detect salicylic acid, a chemical that is produced when potato is injured or infected. With this device, we can know whether the potato is infected immediately at home.</p>
 
<br><br>
 
<br><br>
<p>  We choose to utilize the mtr (metal reduction) pathway of Shewanella oneidensis MR-1. Shewanella oneidensis MR-1 is a gram negative bacteria that is widely used for constructing microbial fuel cells because how it produce electricity is well characterized. The mtr pathway contains 4 proteins: CymA, mtrA, mtrB, mtrC. CymA is a transmembrane protein that can transport electrons out of the cell membrane, and can then activate proteins mtrA, B, and C consecutively. From literature research, mtrB gene plays a pivotal role in stabilizing other component in this pathway. Therefore, in our project we utilize mtrB to create our biosensor by detecting changes in electric signals.
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<p>  We choose to utilize the mtr (metal reduction) pathway of <i>Shewanella oneidensis</i> MR-1. <i>Shewanella oneidensis</i> MR-1 is a gram negative bacteria that is widely used for constructing microbial fuel cells because how it produce electricity is well characterized. The mtr pathway contains 4 proteins: CymA, mtrA, mtrB, mtrC. CymA is a transmembrane protein that can transport electrons out of the cell membrane, and can then activate proteins mtrA, B, and C consecutively. From literature research, mtrB gene plays a pivotal role in stabilizing other component in this pathway. Therefore, in our project we utilize mtrB to create our biosensor by detecting changes in electric signals.
 
</p>
 
</p>
  
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<p>To create this long term biosensor, we first knock out the endogenous mtrB gene in Shewanella oneidensis MR-1. By reintroducing mtrB gene under the control of sensor (nahR), we can control the bacteria to generate electricity when it detects salicylic acid.</p>
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<p>To create this long term biosensor, we acquired <i>S. oneidensis</i> MR-1 (JG700), a strain with the endogenous mtrB gene knocked out from the bacterial genome, from Professor Jeffrey A. Gralnick (University of Minnesota BioTechnology Institute). By reintroducing mtrB gene under the control of sensor (nahR), we can control the bacteria to generate electricity when it detects salicylic acid.</p>
 
<br><br>
 
<br><br>
 
<p>We managed to design our sensor system using mtrB. However, the mere expression of mtrB is not enough. As electric signals in the soil can also be detected by the soil-based microbial fuel cell we created and can constitutively produce electric currents. This electric signal caused by soil itself somehow come as a background noise. When the electric signals emitted by the plant is not intense enough or when the number of bacteria in the soil drops, users may easily confuse it with the signals produced by the soil and make wrong judgments on pathogen control. Thus, we incorporate the oscillator into our circuit design. Even when the currents are not strong enough, users can still easily tell the difference between the electric signals emitted by the soil and those by the infected plant by recognizing the oscillating pattern of the latter.
 
<p>We managed to design our sensor system using mtrB. However, the mere expression of mtrB is not enough. As electric signals in the soil can also be detected by the soil-based microbial fuel cell we created and can constitutively produce electric currents. This electric signal caused by soil itself somehow come as a background noise. When the electric signals emitted by the plant is not intense enough or when the number of bacteria in the soil drops, users may easily confuse it with the signals produced by the soil and make wrong judgments on pathogen control. Thus, we incorporate the oscillator into our circuit design. Even when the currents are not strong enough, users can still easily tell the difference between the electric signals emitted by the soil and those by the infected plant by recognizing the oscillating pattern of the latter.
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<h3>Characterization of salicylic acid induced promoter BBa_J61051</h3>
 
<h3>Characterization of salicylic acid induced promoter BBa_J61051</h3>
  
<p>Before we test our main circuit as shown above, we want to test if the promoter we choose, J61051, is sensitive enough to detect the salicylic acid produced by potato plant when infected, which is about 10<sup>-5</sup>. We induced the promoter with salicylic acid when the O.D.600 of E.coli reach 0.6. The picture bellow shows that when the concentration of salicylic acid is above 10<sup>-5</sup>, the promoter will reach it's maximal activity. Thus, inducible promoter J61051 is right choice for us to use as a salicylic acid sensor.</p>
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<p>Before we test our main circuit as shown above, we want to test if the promoter we choose, J61051, is sensitive enough to detect the salicylic acid produced by potato plant when infected, which is about 10<sup>-5</sup>M. We induced the promoter with salicylic acid when the O.D.600 of E.coli reached 0.6. The picture below shows that when the concentration of salicylic acid is above 10<sup>-5</sup>, the promoter will reach it's maximal activity. Thus, inducible promoter J61051 is the suitable choice for us as a salicylic acid sensor.</p>
  
 
<img src="https://static.igem.org/mediawiki/2015/f/f4/Nymu-taipei-dose_response.jpeg" style="padding-left:18%;padding-top:3%;width:43%; padding-bottom:2%">  
 
<img src="https://static.igem.org/mediawiki/2015/f/f4/Nymu-taipei-dose_response.jpeg" style="padding-left:18%;padding-top:3%;width:43%; padding-bottom:2%">  
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<p>After we build the main circuit, oscillator, we first test its function with GFP as reporter. We carry out the experiment by adding different concentrations of salicylic acid(0, 10<sup>-6</sup>, 10<sup>-5</sup>, 10<sup>-4</sup>) and record the GFP florescence intensity. The induction of J61051 promoter started when the O.D. is 0.4. The GFP florescence intensity is measured by microplate reader every 15 minutes. After a overnight incubation, the bacteria were induced by different concentrations of salicylic acid(0, 10<sup>-6</sup>, 10<sup>-5</sup>, 10<sup>-4</sup>).<p>  
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<p>After we build the main circuit, oscillator, we first test its function with GFP as reporter. We carry out the experiment by adding different concentrations of salicylic acid(0, 10<sup>-6</sup>, 10<sup>-5</sup>, 10<sup>-4</sup>) and record the GFP florescence intensity. The induction of J61051 promoter started when the O.D. is 0.4. The GFP florescence intensity is measured by microplate reader every 15 minutes. After an overnight incubation, the bacteria were induced by different concentrations of salicylic acid(0, 10<sup>-6</sup>, 10<sup>-5</sup>, 10<sup>-4</sup>).<p>  
  
 
<img src="https://static.igem.org/mediawiki/2015/e/ef/Nymuosc.jpg" style="padding-top:3%;width:70%; padding-bottom:2%">
 
<img src="https://static.igem.org/mediawiki/2015/e/ef/Nymuosc.jpg" style="padding-top:3%;width:70%; padding-bottom:2%">
  
<p>As shown in the picture above. The oscillation of GFP exists even when there is no salicylic acid, this is maybe due to the leakiness of the J61051 inducible promoter. However, the amplitude and duration of the oscillation is much higher when the bacteria is iduced with 10<sup>-6</sup> of salicylic acid. ALthough the production of GFP keeps building up, which makes the oscillation not so perfect, the oscillation is stable even after 500 minutes after induction. </p><br>
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<p>As shown in the picture above. The oscillation of GFP exists even when there is no salicylic acid, this is maybe due to the leakiness of the J61051 inducible promoter. However, the amplitude and duration of the oscillation is much higher when the bacteria is induced with 10<sup>-6</sup> of salicylic acid. Although the production of GFP keeps building up, which makes the oscillation not so perfect, the oscillation is stable even after 500 minutes after induction. </p><br>
  
<p>From paper research, we learned that the salicylic acid produced by potatoes is about 10<sup>-5</sup> M. The oscillation with that concentration of salicylic acid is show on the picture bellow. The period of the oscillation is about 80 minutes</p>
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<p>From paper research, we learned that the salicylic acid produced by potatoes is about 10<sup>-5</sup> M. The oscillation with that concentration of salicylic acid is show on the picture below. The period of the oscillation is about 80 minutes</p>
  
 
<img src="https://static.igem.org/mediawiki/2015/4/4b/Nymusoa.jpg" style="padding-top:3%;width:48%; padding-left:18%; padding-bottom:2%">
 
<img src="https://static.igem.org/mediawiki/2015/4/4b/Nymusoa.jpg" style="padding-top:3%;width:48%; padding-left:18%; padding-bottom:2%">
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<p>To test that whether our part will work, we need to put the whole construct into Shewanella JG700, an mtrB  knockout strain, since mtrB can only work in Shewanella. We carry out conjugation experiment to transfer the plasmid into Shewanella. First, we  insert our salicylic acid construct into the PBBR1-MCS2 cloning vector. The plasmid contains a mobility gene that can facilitate the transfer of plasmid. We then transform the vector PBBR1-MCS2 into E.coli S-17. Last, we perform the conjugation by mating E.coli S-17 and Shewanella JG700</p>  
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<p>To test that whether our part will work, we need to put the whole construct into <i>S. oneidensis</i> MR-1 JG700, an mtrB  knock-out strain, since mtrB can only work in Shewanella. We carry out conjugation experiment to transfer the plasmid into Shewanella. First, we  insert our salicylic acid construct into the PBBR1-MCS2 cloning vector. The plasmid contains a mobility gene that can facilitate the transfer of plasmid. We then transform the vector PBBR1-MCS2 into <i>E.coli</i> S-17. Last, we perform conjugation on <i>E.coli</i> S-17 and <i>S. oneidensis</i> MR-1 JG700</p>  
 
<br><br>
 
<br><br>
<p>The gel image below shows that we success in transformming our biobricks(<a href="http://parts.igem.org/Part:BBa_K1769002">BBa_K1769002</a> <a href="http://parts.igem.org/Part:BBa_K1769003">BBa_K1769003</a>) into Shewanella JG700 by conjugation. However, we didn't have enough time to test the function of these biobricks in Shewanella.</p><br>
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<p>The gel image below shows that we successfully transformed our biobricks(<a href="http://parts.igem.org/Part:BBa_K1769002">BBa_K1769002</a> <a href="http://parts.igem.org/Part:BBa_K1769003">BBa_K1769003</a>) into <i>S. oneidensis</i> MR-1 JG700 by conjugation. However, we didn't have enough time to test the function of these biobricks in Shewanella.</p><br>
  
  
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<ul>
 
<ul>
 
<li>Inoculation of tomato plant:  
 
<li>Inoculation of tomato plant:  
Tomato plants were used when they had grown five to six leaves. Freshly produced sporangia were harvested in glass-distilled water (4°C) and used for inoculation. Tomato were inoculated by spraying the upper and lower surfaces of the leaves with 3ml sporangia suspension of P. infestans, and the optimum concentration for tomato is about 2500-5000sporangia/ml . The inoculated plants were placed in a moist condition at 20°C. Late blight symptoms development were recorded seven days after inoculation by counting the number and measuring the size of lesion and blighted area.<li>
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Tomato plants were used when they had grown five to six leaves. Freshly produced sporangia were harvested in glass-distilled water (4°C) and used for inoculation. Tomato were inoculated by spraying the upper and lower surfaces of the leaves with 3ml sporangia suspension of P. infestans, and the optimum concentration for tomato is about 2500-5000 sporangia/ml . The inoculated plants were placed in a moist condition at 20°C. Late blight symptoms development were recorded seven days after inoculation by counting the number and measuring the size of lesion and blighted area.<li>
  
 
<li>Inoculation of potato plant:  
 
<li>Inoculation of potato plant:  
Potato plants were used when they had grown nine to eleven leaves. Freshly produced sporangia were harvested in glass-distilled water (4°C) and used for challenge inoculation. Potato were inoculation by spraying the lower surface of leaves with 5ml sporangia suspension of P.infestans, and the optimum concentration for tomato is about 4000-7500sporangia/ml. The inoculated plants were placed in a moist condition at 20 °C. Late blight systems development were record seven days after inoculation by counting the number and measuring the size of lesion and blighted area.</li>
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Potato plants were used when they had grown nine to eleven leaves. Freshly produced sporangia were harvested in glass-distilled water (4°C) and used for challenge inoculation. Potato were inoculation by spraying the lower surface of leaves with 5ml sporangia suspension of P.infestans, and the optimum concentration for tomato is about 4000-7500 sporangia/ml. The inoculated plants were placed in a moist condition at 20 °C. Late blight systems development were record seven days after inoculation by counting the number and measuring the size of lesion and blighted area.</li>
  
 
</ul>
 
</ul>
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<p>Generally we were expecting the plants sprayed with defensin develop smaller areas of lesions than the untreated ones; our preliminary results partially agreed with our hypothesis, however, in certain cases it may appear otherwise. We discussed this problem with local research institutes, and found out that water content in the defensin spray may also make the plant more susceptible, and other fungal infections should be taken into account as well, as they may also make plants less defensive against pathogen invasion. We would want to purify defensin in the future to solve this problem. There certainly is more to discuss and look into in this experiment, and we would definitely consult experts and others to gather more information on our future applications.</p>
  
  
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<p>This model was designed to investigate the competitive binding between FYVE protein domain and PI3P. Before we construct the circuit with FYVE, we have to determine whether the affinity between FYVE and PI3P is strong enough to compete with Avr3 effector protein secreted by <i>P. infestans</i> . We characterize this agonist-antagonist competition model by Gaddum-Schild equation.  
 
<p>This model was designed to investigate the competitive binding between FYVE protein domain and PI3P. Before we construct the circuit with FYVE, we have to determine whether the affinity between FYVE and PI3P is strong enough to compete with Avr3 effector protein secreted by <i>P. infestans</i> . We characterize this agonist-antagonist competition model by Gaddum-Schild equation.  
This model consists of two parts: monomeric FYVE and dimeric FYVE. According to paper research, monomeric FYVE is not so stable but the affinity of both monomeric and dimeric FYVE is quite high. The model is used to determine how much FYVE protein domain we should link with a linker so that the affinity of FYVE will be high enough to compete with Avr3.
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This model consists of two parts: monomeric FYVE and dimeric FYVE. According to paper research, monomeric FYVE is not so stable but the affinity of both monomeric and dimeric FYVE is quite high. The model is used to determine how many FYVE protein domain monomers we should link with a linker so that the affinity of FYVE will be high enough to compete with Avr3.
 
<br>
 
<br>
 
<img src="https://static.igem.org/mediawiki/2015/5/54/Nymu-Fyve_equation.png"  >
 
<img src="https://static.igem.org/mediawiki/2015/5/54/Nymu-Fyve_equation.png"  >
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<br><br>
 
<br><br>
 
<p>
 
<p>
These two graphs are the modeling of the competitive inhibition between FYVE domain and Avr3. The left graph shows that the monomeric FYVE can only occupies less than 10 percent of the PI3P receptor, but on the right graph it shows apparently that the dimeric FYVE occupies more than 80 percent of the receptor, which is a suitable competitive inhibitor that fights against the Avr3.
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These two graphs are the modeling of the competitive inhibition between FYVE domain and Avr3. The left graph shows that the monomeric FYVE can only occupy less than 10 percent of the PI3P phospholipid, but on the right graph it shows apparently that the dimeric FYVE occupies more than 80 percent of PI3P, which is a suitable competitive inhibitor that fights against the Avr3.
 
</p>
 
</p>
  
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<p>
 
<p>
As we can see in the models dimeric FYVE is not only more stable but also have higher affinity to compete with avr3, the effector protein secreted by <i>P. infestans</i> . To create FYVE protein domain with higher affinity, we can link as much FYVE domain artificially as we want. However, it takes a lot of time to add a linker protein between FYVE and connect two FYVE together. Moreover, the longer the FYVE is, the longer it takes for the palnt to degrade the protein we created, which might have a negative impact to the potato. By constructing this model, we can find out the simplest way to create a competitive inhibitor and save the time for trial and error.
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As we can see in the models dimeric FYVE is not only more stable but also have higher affinity to compete with Avr3, the effector protein secreted by <i>P. infestans</i> . To create FYVE protein domain with higher affinity, we can link as much FYVE domain artificially as we want. However, it takes a lot of time to add a linker protein between FYVE and connect two FYVE together. Moreover, the longer the FYVE is, the longer it takes for the plant to degrade the protein we created, which might pose a negative impact on the potato. By constructing this model, we can find out the simplest way to create a competitive inhibitor and save the time for trial and error.
  
  
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<p>We construct a Lux/Aiia quorum-sensing oscillator so that S. oneidensis-MR1 will generate oscillating current which can help farmers tell if the potatoes are infected. We build two models to characterize the outcome of the genetic oscillator. The first model is used to predict whether the genetic oscillator will work. Since it is possible that some of the engineered S. oneidensis we inoculated on anode won’t survive, the second model is used to predict whether the gene expression will oscillate when there are only a few bacteria left on the anode.
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<p>We construct a Lux/Aiia quorum-sensing oscillator so that <i>S. oneidensis</i> MR1 will generate oscillating current which can help farmers tell if the potatoes are infected. We build two models to characterize the outcome of the genetic oscillator. The first model is used to predict whether the genetic oscillator will work. Since it is possible that some of the engineered <i>S. oneidensis</i> inoculated on anode will not survive, the second model is used to predict whether the gene expression will oscillate when there are only a few bacteria left on the anode.
  
  
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<p>Since our circuit design is based on the circuit published by Danino et al.[1] our first model is a slight modification of the equation from the supplementary information. This model consists of four delay differential equation. We added one more delay differential equation to this model to simulate the expression of MtrB gene.
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<p>Since our circuit design is based on the circuit published by Danino et al.[1], our first model is a slight modification of the equation from the supplementary information. This model consists of four delay differential equation. We added one more delay differential equation to this model to simulate the expression of MtrB gene.
  
 
</p>
 
</p>
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<h2 style="padding-top:8%;">Conclusion</h2>
 
<h2 style="padding-top:8%;">Conclusion</h2>
 
<p style="padding-bottom:1%">
 
<p style="padding-bottom:1%">
As you can see in the graph, the production of Aiia and LuxI will oscillate as the time goes by. However, the production of MtrB will build up instead of oscillating. This is because MtrB is too stable and it is not totally degraded before the next period of oscillation. Thus, it is necessary to be targeted for proteolysis by adding a degradation tag in MtrB coding sequence.
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As you can see in the graph, the production of Aiia and LuxI will oscillate as the time goes by. However, the production of MtrB will build up instead of oscillating. This is because MtrB is too stable and it is not totally degraded before the production of new MtrB. Thus, it is necessary to target MtrB for proteolysis by adding a degradation tag to its coding sequence.
  
 
</p>
 
</p>
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<p>
 
<p>
If we inoculate the engineered S. oneidensis MR-1 into the anode of our SMFC, chances are that some of the bacteria will not survive in soil. Thus, it is necessary to simulate if the oscillator will work with only a single bacteria or a small population of bacteria.
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If we inoculate the engineered <i>S. oneidensis</i> MR-1 into the anode of our SMFC, chances are that some of the bacteria will not survive in soil. Thus, it is necessary to simulate if the oscillator will work with only a single bacteria or a small population of bacteria.
 
<br><br>
 
<br><br>
With a small amount of bacteria, there will not have a significant time delay, and that’s why we chose a set of ODE to simulate the genetic oscillator.
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With a small amount of bacteria, there will not be a significant time delay, and that is why we chose a set of ODE (rather than DDE) to simulate the genetic oscillator.
 
<br>
 
<br>
 
</p>
 
</p>
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<p>
 
<p>
As we can see in this model, even if there is only a small amount of bacteria left on the anode, the oscillator can still work. However, the current output will be much lesser than that of a large population of bacteria.
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As we can see in this model, even if there is only a small amount of bacteria left on the anode, the oscillator can still work. However, the current output will be much less than that of a large population of bacteria.
  
 
</p>
 
</p>
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<p><i>P. infestans</i> is an obligate parasite, which means it can’t complete its life cycle without exploiting potato. However, if a potato is infected by <i>P. infestans</i>, the disease will spread through the potato farm within 20 days.
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<p><i>P. infestans</i> is an obligate parasite, which means it cannot complete its life cycle without exploiting potatoes. However, if a potato is infected by <i>P. infestans</i>, the disease will spread through the potato farm within 20 days.
 
<br><br>
 
<br><br>
In this model, we try to show the difference between planting the potato we engineered and planting potato that is susceptible or moderately resistant to potato late blight. Traditionally, SEIR model will be the perfect model to characterize epidemiology. However, we made some changes to the assumption of the traditional SEIR model so that the model can reflect the reality of potato late blight epidemiology much better. </p>
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In this model, we try to show the difference between planting the potato we engineered and planting potato that is susceptible or moderately resistant to potato late blight. Traditionally, SEIR model will be the perfect model to characterize epidemiology. However, we made some modifications to the assumption of the traditional SEIR model so that it can reflect the reality of potato late blight epidemiology much better. </p>
  
 
<img src="https://static.igem.org/mediawiki/2015/9/9b/Nymu-drylab-epidemic_1.jpg" style="padding-left:15%">
 
<img src="https://static.igem.org/mediawiki/2015/9/9b/Nymu-drylab-epidemic_1.jpg" style="padding-left:15%">
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<tr>
 
<tr>
   <td rowspan="5">Epidemology</td><td> Symbol  </td> <td> Meaning  </td> </tr> <tr> <td> Suspected </td> <td> The potato that is slightly or moderately resistant to <i>P. infestans</i>  </td> </tr> <tr> <td> Exposed </td> <td> Potatoes that are infected by a low amount of <i>P. infestans</i> , but they are still curable andwill not spread the disaese </td> </tr> <tr> <td> Infected </td> <td> Potatoes that are infected by a large amount of <i>P. infestans</i> . Potatoes infected will spread the disease and jeopardize the whole potato farm </td> </tr> <tr> <td> Immunized </td> <td> Potatoes that has been transformed by us and is resistant against <i>P. infestans</i>  </td> </tr></table>
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   <td rowspan="5">Epidemology</td><td> Symbol  </td> <td> Meaning  </td> </tr> <tr> <td> Susceptible </td> <td> The potato that is slightly or moderately resistant to <i>P. infestans</i>  </td> </tr> <tr> <td> Exposed </td> <td> Potatoes that are infected by a low amount of <i>P. infestans</i> are still curable; further spread of the disease will not occur</td> </tr> <tr> <td> Infected </td> <td> Potatoes that are infected by a large amount of <i>P. infestans</i> . Potatoes infected will spread the disease and jeopardize the whole potato farm </td> </tr> <tr> <td> Immunized </td> <td> Potatoes that has been transformed by us and is resistant against <i>P. infestans</i>  </td> </tr></table>
 
</div>
 
</div>
 
<p>According to our assumptions, we developed a set of ordinary differential equations that can characterize the epidemic of potato late blight.</p>
 
<p>According to our assumptions, we developed a set of ordinary differential equations that can characterize the epidemic of potato late blight.</p>
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<h2>Result</h2>
 
<h2>Result</h2>
 
<p style="padding-bottom:1%">
 
<p style="padding-bottom:1%">
As we can see from the model,with our genetically modified potatoes can prevent 80% of the potatoes from being infected, while only 20% of the moderately resistant potatoes can survive during the epidemic of potato late blight.  
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As we can see from the model, with our genetically modified potatoes can prevent 80% of the potatoes from being infected while only 20% of the moderately resistant potatoes can survive during the epidemic of potato late blight.  
Also as we can see in the graphs above, the survival rate do fit our sassumption the the potatoes under the destruction of late blight only end in two consequences----death(once the potatoes exposed to small amount of <i>P. infestans</i>  are seriously infected ) or survive (once the “exposed potato” truned into “immunized potato”).  
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Also as we can see in the graphs above, the survival rate do fit our assumption the the potatoes under the destruction of late blight only have two obvious consequences----death(once the potatoes exposed to small amount of <i>P. infestans</i>  are seriously infected) or survival (once the “exposed potato” turned into “immunized potato”).  
This graphs not only show that our potatoes might be effective in fighting potato late blight in the real world but also show that potato late blight is a disease that needs to be take care of seriously and a impeccible system is necessary to fight against the disease.
+
This graphs not only show that our potatoes might be effective in fighting potato late blight in the real world but also show that potato late blight is a disease that needs to be take care of seriously and a impeccable system is necessary to fight against the disease.
  
 
</p>
 
</p>
Line 961: Line 961:
  
  
<h2>Conclusion</h2>
 
<p>As we can see from the model,with our genetically modified potatoes can prevent 80% of the potatoes from being infected, while only 20% of the moderately resistant potatoes can survive during the epidemic of potato late blight.
 
Also as we can see in the graphs above, the survival rate do fit our sassumption the the potatoes under the destruction of late blight only end in two consequences----death(once the potatoes exposed to small amount of <i>P. infestans</i>  are seriously infected ) or survive (once the “exposed potato” truned into “immunized potato”).
 
This graphs not only show that our potatoes might be effective in fighting potato late blight in the real world but also show that potato late blight is a disease that needs to be take care of seriously and a impeccible system is necessary to fight against the disease.
 
</p>
 
  
  
Line 983: Line 978:
  
  
<p>Our defense system contains three different: prevention, detection, and cure. So how do we connect this three part together to form an impeccable defense system? First, we plant the genetically modified potato in the farm to prevent the disease from infecting the whole farm in a short period of time. Secondly, we implement the soil based microbial fuel cell that can detect and report whether the potato is infected or not immediately, in case that the potato can’t fend of the disease. When the SMFC detects the disease, it will send an electric signal that can trigger the spraying system. The spraying system will then spray the environmentally friendly defensin that we produced automatically. Man power is not required in the whole process except planting the potatoes.Also, the defense system will also connect to a phone app that we created, so that we can know whether the potatoes are healthy or not. The defense system is localized, socialized, and mobilized.
+
<p>Our defense system contains three different stages: prevention, detection, and cure. So how do we connect this three part together to form an impeccable defense system? First, we plant the genetically modified potatoes in the farm to prevent the disease from infecting the whole farm in a short period of time. Secondly, since no prevention strategy can be a hundred percent effective, we implement the soil based microbial fuel cell that can detect and report whether the potato is infected or not immediately. When the SMFC detects the disease, it will send an electric signal that can trigger the spraying system. The spraying system will then spray the environmentally friendly defensin that we produced automatically. Man power is not required in the whole process except planting the potatoes. Also, the defense system will also connect to a phone app that we created, so that we can know whether the potatoes are healthy or not. The defense system is localized, socialized, and mobilized.
 
<a href="https://2015.igem.org/Team:NYMU-Taipei/Design">See our SMFC page for more info</a>
 
<a href="https://2015.igem.org/Team:NYMU-Taipei/Design">See our SMFC page for more info</a>
 
</p>
 
</p>

Latest revision as of 23:36, 20 November 2015

Experiment

Prevention

Concept

After P. infestans penetrates the cell wall of potato, it will exploit the potato and in turn infect potatoes nearby within 3 days. Since it will infect other potatoes in such a short time that there is no effective biological method to react against it and inhibit the development of the disease, we decide to prevent the disease at the very beginning by rendering the potatoes the ability to prevent the invasion of P. infestans.



Under certain conditions, the zoospores of P. infestans will attach to the surface of potato leaves, penetrate the cell wall with high turgor pressure and some enzymes as well as secrete effector proteins (Eg. Avr3) into potato cells. The binding of the effector protein to the transmembrane phospholipid PI3P is essential and helps mediate the translocation of the effector protein into the potato cell. It suppresses plant resistance-gene-based immunity; thus, P. infestans enter potato cells without any resistance.



To stop the effector protein from entering potato cell, we found through literature research that reduction in translocated effector is a promising way to decrease the virulence of pathogens and improve disease resistance in potatoes. In previous research, FYVE protein domain from Hrs or EEA1 can also bind to PI3P phospholipid strongly in animal cells. We then decided to construct a FYVE protein domain with high affinity to PI3P that can compete with the effector protein to inhibit the entry of P. infestans.

Oomycete and potato cell
Effector protein binding with PI3P and entering
Dimeric FYVE preventing effector from binding with PI3P

FYVE protein domain

Dimeric FYVE protein

The FYVE protein domain is a well-conserved PI3P-binding domain in various organisms with only 78 amino acids, originally existing in EEA1 and Hrs proteins of human and mouse, respectively, but also in many other proteins. However, according to literature research, EEA1 and Hrs protein are too large and may take a long time for the plant to degrade, so we extracted only the FYVE domain from Hrs protein, whose ability of binding to PI3P was better than others. Yet the monomeric FYVE is relatively unstable in the cytoplasm, so we constructed a dimeric FYVE to improve the stability.

Experimental Design

We chose dimeric FYVE as our competitive inhibitor against the Avr3 secreted by P. infestans. We first conducted modeling simulation, and the dissociation constant of monomeric FYVE was far higher than the RXLR of Avr3 and was quite unstable. After connecting two FYVE domains with a linker protein and thus enhancing the affinity and stability of the substrate, the dissociation constant was lowered from 250 to 3.8.

FYVE inhibition test

As shown below in the FYVE inhibition test



This model was designed to investigate the competitive binding between FYVE protein domain and PI3P. Before we construct the circuit with FYVE, we have to determine whether the affinity between FYVE and PI3P is strong enough to compete with Avr3 effector protein secreted by P. infestans.



As we can see in the models, dimeric FYVE is not only more stable but also have higher affinity to compete with Avr3. By constructing this model, we can find out the simplest way to create a competitive inhibitor and save the time for trial and error.

The Construction of dimeric FYVE

To construct dimeric FYVE, we designed primers containing enzyme cutting sites "EcoRI, XbaI, SpeI, PstI" and coding sequence of the linker protein to extract monomeric FYVE domain from the Mus musculus embryonic cDNA. We then used restriction enzyme cloning method to insert the dimeric FYVE into pSB1C3.

Circuit construction

pSAT6-EYFP-N1 is a vector which can express foreign genes in plant cells through transfection. Therefore, we designed a set of primers to extract monomeric and dimeric FYVE from pSB1C3 and transfer it into the vector for transfection. The circuit is displayed below, containing viral constitutive promoter CaMV35S followed by the coding sequence of dimeric FYVE. The coding sequence of the enhanced yellow fluorescence protein (EYFP) follows that of the dimeric FYVE as a reporter gene to indicate whether the dimeric FYVE works well in the plant.

Dimeric FYVE circuit

Transfection of plasmid DNA into plant cells

To check whether dimeric FYVE functions properly in plant cells, we inserted dimeric FYVE into vector PSAT6–EYFP-N1 and transfect the recombinant plasmid DNA into tobacco protoplasts, BY-2. BY-2 cells and potato cells share many common biological features as tobacco (Nicotiana tabacum) and potato (Solanum tuberosum) both belong to the Solanaceae family. If the dimeric FYVE works well in BY-2 cell, we will see yellow fluorescence on the endosome of the cell.



Then, we fused the cell with Wortmannin, a PI3K inhibitor restraining the synthesis of PI3P. If the binding between dimeric FYVE and PI3P is effective, inhibiting the production of PI3P will lead to the redistribution of dimeric FYVE into cytoplasm. To check if the dimeric FYVE actually binds to PI3P, we compared the engineered BY-2 cells containing dimeric FYVE and one that did not, with only the experiment group treated with Wortmannin. The one treated with Wortmannin showed a dispersion of yellow fluorescence, while the other displayed emission of concentrated light around the PI3P phospholipid. With this experiment, we can confirm that dimeric FYVE not only expresses in the engineered BY-2 protoplast but also binds to the PI3P phospholipid as expected.

Results

  1. Monomeric and dimeric FYVE gel images (plate)
  2. We first extracted the FYVE domain from the Mus musculus embryonic cDNA and put it in the PSB1C3 vector. The gel image(A) displaying the correct band size of 232 base pairs indicated the successful cloning result. Secondly, we used restriction enzyme cloning to put in the second FYVE domain to construct dimeric FYVE. The gel image(B) showing the band with length of 454 bp indicated the construction of dimeric FYVE was successful. We designed a set of primers with new cutting sites, XhoI and HindIII , to extract the dimeric FYVE from PSB1C3 and transferred it into another vector, PSAT6-EYFP-N1. The gel image(C) with the band size of 433 bp indicated that the result was successful.

    image a
    image b
    image c
  3. Dimeric FYVE binding test
  4. The preparation of protoplast was conducted in the transgenic plant core lab of Academia Sinica. The picture on the right indicates the By-2 cells processed with the digestion of enzymes Cellulase Onozuka RS and Macerozyme R-10.

    We were ready to add enzyme solution into By-2 cells.
    After adding the enzyme solution, we removed the liquid into a plastic petri dish. It was then kept on a horizontal shaker at 30 rpm in darkness at room temperature.
    The By-2 cells processed by enzyme solution became protoplasts. What we were about to do here was to centrifuge the solution.
    BY-2 cell with empty PSAT6 cloning vector
    BY-2 cell expressing dimeric FYVE+GFP fusion protein. The fusion protein concentrated on cell membrane and endosome
    BY-2 cell expressing dimeric FYVE+GFP fusion protein and treated with Wortmannin, the PI3K inhibitor. The fusion protein redistributed into the cytoplasm

    After the PEG transfection steps, we then incubated the cells in darkness at 26℃ for 16 hours. The pictures below are the protoplasts expressing fluorescence. It indicated the successful transfection of plasmid DNA into By-2 cells.

    In the competitive inhibitor binding test, we first compared BY-2 cells containing only an empty pSAT6 vector expressing only EYFP with the experiment group containing the dimeric FYVE fused with EYFP. The picture (a) showed that the cell without dimeric FYVE had dispersion of fluorescence on the whole cell, but the picture (b) with dimeric FYVE showed the concentrated light around the endosomes containing PI3P phospholipids. Secondly, we fused 1 ul Wortmannin, an inhibitor of the synthesis of PI3P, with 100ul protoplasts. The one treated with Wortmannin obviously showed the dispersion of fluorescence(pic). We took a series of photos every 5 minutes after treating the protoplasts with the inhibitor. With the comparison between these photos, the results strongly suggest that dimeric FYVE not only expresses within the engineered BY-2 protoplasts but also binds to the PI3P phospholipid.

Detection

Introduction

One major problem in controlling potato late blight is that there is no simple and convenient way to detect the disease. If we want to make sure that our potato has not been infected by P. infestans , we have to examine the potato in the laboratory for approximately a week. We want our detection system to efficient and can instantly report the infection to the user. So the solution we reached is to design a soil based microbial fuel cell (SMFC) that can detect salicylic acid, a chemical that is produced when potato is injured or infected. With this device, we can know whether the potato is infected immediately at home.



We choose to utilize the mtr (metal reduction) pathway of Shewanella oneidensis MR-1. Shewanella oneidensis MR-1 is a gram negative bacteria that is widely used for constructing microbial fuel cells because how it produce electricity is well characterized. The mtr pathway contains 4 proteins: CymA, mtrA, mtrB, mtrC. CymA is a transmembrane protein that can transport electrons out of the cell membrane, and can then activate proteins mtrA, B, and C consecutively. From literature research, mtrB gene plays a pivotal role in stabilizing other component in this pathway. Therefore, in our project we utilize mtrB to create our biosensor by detecting changes in electric signals.

Experimental Design

To create this long term biosensor, we acquired S. oneidensis MR-1 (JG700), a strain with the endogenous mtrB gene knocked out from the bacterial genome, from Professor Jeffrey A. Gralnick (University of Minnesota BioTechnology Institute). By reintroducing mtrB gene under the control of sensor (nahR), we can control the bacteria to generate electricity when it detects salicylic acid.



We managed to design our sensor system using mtrB. However, the mere expression of mtrB is not enough. As electric signals in the soil can also be detected by the soil-based microbial fuel cell we created and can constitutively produce electric currents. This electric signal caused by soil itself somehow come as a background noise. When the electric signals emitted by the plant is not intense enough or when the number of bacteria in the soil drops, users may easily confuse it with the signals produced by the soil and make wrong judgments on pathogen control. Thus, we incorporate the oscillator into our circuit design. Even when the currents are not strong enough, users can still easily tell the difference between the electric signals emitted by the soil and those by the infected plant by recognizing the oscillating pattern of the latter.

Main circuit

As the circuit showed below, we first tested the oscillator and sensor with GFP as reporter. Then we replaced GFP with MtrB to test the expression of MtrB will also oscillate.

Results

Characterization of salicylic acid induced promoter BBa_J61051

Before we test our main circuit as shown above, we want to test if the promoter we choose, J61051, is sensitive enough to detect the salicylic acid produced by potato plant when infected, which is about 10-5M. We induced the promoter with salicylic acid when the O.D.600 of E.coli reached 0.6. The picture below shows that when the concentration of salicylic acid is above 10-5, the promoter will reach it's maximal activity. Thus, inducible promoter J61051 is the suitable choice for us as a salicylic acid sensor.

Experimental data of our main circuit

After we build the main circuit, oscillator, we first test its function with GFP as reporter. We carry out the experiment by adding different concentrations of salicylic acid(0, 10-6, 10-5, 10-4) and record the GFP florescence intensity. The induction of J61051 promoter started when the O.D. is 0.4. The GFP florescence intensity is measured by microplate reader every 15 minutes. After an overnight incubation, the bacteria were induced by different concentrations of salicylic acid(0, 10-6, 10-5, 10-4).

As shown in the picture above. The oscillation of GFP exists even when there is no salicylic acid, this is maybe due to the leakiness of the J61051 inducible promoter. However, the amplitude and duration of the oscillation is much higher when the bacteria is induced with 10-6 of salicylic acid. Although the production of GFP keeps building up, which makes the oscillation not so perfect, the oscillation is stable even after 500 minutes after induction.


From paper research, we learned that the salicylic acid produced by potatoes is about 10-5 M. The oscillation with that concentration of salicylic acid is show on the picture below. The period of the oscillation is about 80 minutes

Conjugation

To test that whether our part will work, we need to put the whole construct into S. oneidensis MR-1 JG700, an mtrB knock-out strain, since mtrB can only work in Shewanella. We carry out conjugation experiment to transfer the plasmid into Shewanella. First, we insert our salicylic acid construct into the PBBR1-MCS2 cloning vector. The plasmid contains a mobility gene that can facilitate the transfer of plasmid. We then transform the vector PBBR1-MCS2 into E.coli S-17. Last, we perform conjugation on E.coli S-17 and S. oneidensis MR-1 JG700



The gel image below shows that we successfully transformed our biobricks(BBa_K1769002 BBa_K1769003) into S. oneidensis MR-1 JG700 by conjugation. However, we didn't have enough time to test the function of these biobricks in Shewanella.


Gel image of BBa_K1769003 in vector PBBR1-MCS2
Gel image of BBa_K1769002 in vector PBBR1-MCS2

Reference

  1. Mukhopadhyay, Subhas C., and Joe-Air Jiang, eds. Wireless Sensor Networks and Ecological Monitoring. Vol. 3. Springer Science & Business Media, (2013).
  2. Hartshorne, Robert S., et al. "Characterization of an electron conduit between bacteria and the extracellular environment." Proceedings of the National Academy of Sciences 106.52 (2009)
  3. Coursolle, Dan, and Jeffrey A. Gralnick. "Reconstruction of extracellular respiratory pathways for iron (III) reduction in Shewanella oneidensis strain MR-1." Frontiers in microbiology 3 (2012).
  4. Cooke, Keegan G., et al. "BackyardNetTM: distributed sensor network powered by terrestrial microbial fuel cell technology." SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, (2010)

Cure

Concept

Our goal is to kill P. infestans without harming the potatoes and environment. Fungicides used to kill P. infestans nowadays contain lots of heavy metals, such as Cu2+ that will do harm to both the plant and environment. At first we try to use some antimicrobial peptides; however they will not only kill P. infestans but also do harm to potatoes as well. Therefore, we try to look for the naturally acquired defensin that can weaken or even kill P. infestans from other plants. Although there are other chemicals that might be effective in inhibiting the growth of P. infestans, such as 2, 6-dichlorobenzonitrile (2,6-DCB), but some of these may also do harm to bacteria itself. Based on the reasons above, we finally choose Lm-def, a defensin isolated from maca (Lepidium meyenii) that can effectively weaken and inhibit the growth of P. infestans.

Experimental Design

Circuit construction

The pET expression system originally features a C-terminal 6x His tag. In our circuit design, we use different sets of primers to test on which terminus His-tag is added will optimize the expression of defensing and also to facilitate protein purification. In order to mass produce defensin, we choose T7 promoter to activate the expression of our main biobrick part, lm-def, and transform the entire plasmid into E.coli BL21 (DE3), whose expression of large amounts of recombinant proteins can be induced by the addition of IPTG under the regulation of the native lac operon.

Bacterial Cell Lysis and Protein purification

We incubated liquid culture of the selected colonies at 37 degrees Celsius overnight, and added to it IPTG and kanamycin, for induction and antibiotics selection, respectively, when we transferred 2 mL of liquid culture into fresh LB. We incubated it for another five hours for the OD600 value to reach 0.5 using LB as blank. We then centrifuged it and washed the pellet with 1X PBS. Before we lysed it with liquid homogenization using a continuous high pressure cell disrupter, we added to it protease inhibitor to prevent the cytoplasmic protein from being degraded. The operation of the Cell Disrupter majorly followed the working manual provided by Constant Systems (TS Benchtop 0.75kW). Afterwards, we measured protein concentration of the raw lysate using a spectrophotometer using BSA as blank.

Antimicrobial activity test

Next, to test the activity of defensin against P. infestans in vitro, we incubated in advance the oomycete acquired from Taiwan Agricultural Research Institute on rye agar plates at 18 degrees Celsius for ten days to acquire sporangia and hyphal pieces. Dilution broth solution was then prepared by boiling fresh pea seeds for 20 minutes in distilled water and autoclaved for sterilization. We harvested P. infestans in sterile water; later, we used hemocytometer to count the number of sporangia and diluted it to 250 sporangia per 100 microliter with the dilution broth solution. We took the 100 ul spore suspension and added to it 50 ul of recombinant protein in a 96 well microtiter plate. We used PBS as blank, the pET29b empty vector as protein control, and the spore suspension for comparison. The mixture will then be incubated in darkness at 18 degrees Celsius for an entire day. Spore germination will be measured with a plate reader at 595 nm at the beginning of the test and 24 hours after inoculation. Apart from that, we monitored changes in hyphal growth by physically cutting out agar and placing it in a 12-well tissue culture plate with each well loaded with 1600 ul of lysate while using dilution broth solution as positive control, as it theoretically did not contain any substance that would suppress the growth of the oomycete. Hyphae were stained with trypan blue with lactophenol. We inspected the samples sliced out of the agar every two hours under a light microscope.



Additionally, to test the function of defensin against P. infestans in vivo, we sprayed the mixture of the spore suspension and the cell lysate onto the leaves of tomatoes and potatoes as they both belong to the Solanaceae family and are both susceptible to the late blight disease. We then grow the plant under proper conditions for another week to observe changes in foliar tissues.

Phytophthora infestans inoculation experiment for testing the function of defensin

General guidelines

  • Incubation of P. infestans:hosted on RyeB plate for 10-14 days without light.
  • Renewal of P. infestans:Agar discs (5 mm in diameter) cut from the periphery of the colonies with a sterile cork borer were used to inoculate plates. Each rye B agar plate was inoculated with one disc of inoculum placed at the margin of the plate.
  • Storage of P. infestans: samples were stored at 4°C (up to 24h), or at -20°C for 24 to 72 h.
  • Production of Rye B agar plate: weigh the needed Rye, wash clean by distilled water, sealed with aluminum foil, shake in low speed for 36h. Proportion between Rye and distilled water is 60g:1000ml. After shaking for 36h, water bath for one hour and filtering with gauze. Then add 20.0g of sucrose, 1.0g of CaCO3, 15.0g agar per1000ml. Shaking mix and making the 9cm diameter plate. Store in 2-8°C.
  • Obtain of spore suspension: sporangia from 10 to 14 days old cultures of P. infestans were dislodged into 10 ml of sterile distilled water by swabbing with a sterile bent-glass rod and filter with gauze. And keep on ice for active.
  • Density determination:3ul per drop, made spore suspension fixing piece, observe with optical microscope, count the number, and take the average.


The numbers of sporangia counted under the light microscope (Ratio of active: inactive sporangia)

0hr 8hr 16hr 24hr
None: Dilution broth solution 58:1 57:31 19:6 98:90
pET29b 196:2 13:1 195:0 100:2
Defensin (Lmdef with stop codon) 86:3 212:197 43:55 120:3
Defensin (His-tagged Lmdef with stop codon) 56:0 119:3 23:7 39:3

Whole tuber inoculation

Kennebec is a medium- to late-maturing white potato. It was bred by the USDA and selected by Presque Isle Station, Maine in 1941. Kennebec is not under plant variety protection. This fast-growing variety has high yields. It maintains good quality in storage and is grown for both fresh market use and chipping.



Healthy tubers of Kennebec were washed in water to remove soil, then surface-sterilized by swabbing with 90% ethanol and dried in a laminar flow hood.



Each tuber had a grid, consisting of two sets of four parallel lines orthogonal to each other (∼3 cm long by 1 cm deep), cut into the surface using a sterile scalpel. Each tuber was inoculated via its grid with either a suspension of fungal mycelia and spores, or dilution solution alone for the control group. Inoculated tubers were kept in plastic bags (one tuber per bag), containing a wet paper towel to maintain high relative humidity, at 18°C in darkness until symptoms appeared (1 to 2 weeks)

Potato tuber slice inoculation experiment

Tubers were sliced into 3-mm-thick slices with the aid of an electric kitchen slicer. Tuber discs were prepared from these tuber slices with a 10-mm-diameter cork borer. Discs were thoroughly washed with water, blotted dry, held for 1 h, and placed in 9-cm petri dishes on dry 7-cm-diameter Whatman No. 1 filter paper, 10 discs per dish (unless stated otherwise). Each disc was inoculated with one 10-µl sporangial suspension (2 × 103 per ml), we have four group of experiment. The blank group (no P. infestans, 15ul dilution solution only), experiment group 1(10ul P. infestans +5ul dilution solution), experiment group 2 (P. infestans +0.2uM defensin 5ul), experiment group 3(P. infestans +0.4uM defensin 5ul) and incubated at 20°C in the dark. Care was taken while doing these experiments to avoid bacterial contamination. This was mainly achieved by carefully drying the tuber tissue before inoculation. Observe every 12 hours to detect the infection.



To quantitatively determine the amount of sporangia in tuber discs, the following procedures were undertaken: discs were placed in 1% HCl, boiled on a hot plate for 30 min to dissolve starch grains, washed with water, mounted in 50% glycerol solution on glass slides, covered with a cover slip, pressed gently, and examined with a dissecting microscope at ×40 and ×160 magnification.

Host plant inoculation

Put the plant to be inoculated into a big plastic bag, add a little water to the bottom of the bag to keep the environment moisturized, and inoculate the entire organism thoroughly and evenly with a spray; afterwards, tie up the bag with an elastic band. Observations were made once every two days for a time period of seven to fourteen days to see if symptoms of infection develop on plant tissues.



  1. Blank control group: solely dilution pea broth without pathogen
  2. Experimental group 1: solely P. infestans spore suspension
  3. Experimental group 2: P. infestans with lower concentration of defensin
  4. Experimental group 3: P. infestans with higher concentration of defensin
  • Inoculation of tomato plant: Tomato plants were used when they had grown five to six leaves. Freshly produced sporangia were harvested in glass-distilled water (4°C) and used for inoculation. Tomato were inoculated by spraying the upper and lower surfaces of the leaves with 3ml sporangia suspension of P. infestans, and the optimum concentration for tomato is about 2500-5000 sporangia/ml . The inoculated plants were placed in a moist condition at 20°C. Late blight symptoms development were recorded seven days after inoculation by counting the number and measuring the size of lesion and blighted area.
  • Inoculation of potato plant: Potato plants were used when they had grown nine to eleven leaves. Freshly produced sporangia were harvested in glass-distilled water (4°C) and used for challenge inoculation. Potato were inoculation by spraying the lower surface of leaves with 5ml sporangia suspension of P.infestans, and the optimum concentration for tomato is about 4000-7500 sporangia/ml. The inoculated plants were placed in a moist condition at 20 °C. Late blight systems development were record seven days after inoculation by counting the number and measuring the size of lesion and blighted area.
Interval Spray treatment and cone Number of lesions/plant Lesion size(mm2) Number of Blight Blighted area(mm2)
1h None 0±2 0±200 -- --
5ml P.infestans(4000 spore/ml)only 0±2 0±200 -- --
5ml P.infestans(4000 spore/ml)+6ml defensin(300mg/ml) 0±2 0±200 -- --
5ml P.infestans(4000 spore/ml)+6ml defensin(150mg/ml) 0±2 0±200 -- --
Day7 None 0±2 0±200 -- --
5ml P.infestans(4000 spore/ml)only 2±9 0±2 2(41±plant) 175±plant
5ml P.infestans(4000 spore/ml)+6ml defensin(300mg/ml) 2±16 0±450 14(46±plant) 1250±plant
5ml P.infestans(4000 spore/ml)+6ml defensin(150mg/ml) 0±9 0±250 8(49±plant 600±plant

Table 1: Effect of defensin applied as a spray to tomato plants on late blight development related to the time interval between spray treatment and inoculation

Generally we were expecting the plants sprayed with defensin develop smaller areas of lesions than the untreated ones; our preliminary results partially agreed with our hypothesis, however, in certain cases it may appear otherwise. We discussed this problem with local research institutes, and found out that water content in the defensin spray may also make the plant more susceptible, and other fungal infections should be taken into account as well, as they may also make plants less defensive against pathogen invasion. We would want to purify defensin in the future to solve this problem. There certainly is more to discuss and look into in this experiment, and we would definitely consult experts and others to gather more information on our future applications.

Results

Construction of the circuit built on pET29b

We got the IDT-synthesized part, Lm-def, and inserted it into the plasmid backbone pET29b to carry out functional test. We tested different sets of primers (1. forward primer and reverse primer with stop codon, 2. forward primer and reverse primer without stop codon, 3. forward primer with His-tag and reverse primer with stop codon, 4. forward primer with His-tag and reverse primer without stop codon) to see on which terminus His-tag is added will optimize the results of purification and yield the most product. We successfully transformed our part into E. coli BL21 (DE3).

Growth Rate Assay

To verify if the production of exogenous Lm-def affect the survival of our chassis, we prepare in advance LB broth and flasks; in the LB broth we added in IPTG for induction of protein expression and antibiotics, kanamycin, to be specific. Using Elisa reader, we tested the OD600 value of BL21 (DE3), BL21 (DE3) with pET29b empty vector, BL21 (DE3) with Lm-def (with stop codon) in pET29b, and BL21 (DE3) with Lm-def (without stop codon) in pET29b per 30 minutes to see their growth rate.


As we can see in this picture, the growth of E.coli BL21 is not affected by lm-def produced by itself. Therefore, the amount of E.coli won't decrease when producing lm-def


To test the effect of defensin against P. infestans in vitro, we mainly focused on the changes of hyphal morphology. We monitored changes in hyphal growth by physically slicing out agar for microscopic inspection. As we can see on the photo above. The hyphea of the p.infestans in presence of Lm-Def exhibited an unhealthy state at where extensive septum formation and thinning of the unhealthy hyphae could be observed


Hyphae of p.infestans before treatment
Hyphae of p.infestans after treatment

Additionally, to test the function of defensin against P. infestans in vivo, we sprayed the mixture of the spore suspension and the cell lysate onto the leaves of tomatoes and potatoes after inoculating these lm-def. We compared those treated with defensin with the ones sprayed with only spore suspension.


After one week, we discover dark lesions on the leaves of the plant and observed the tissue under the light microscope. We discover that the infection was more severe on the plant treated with lower concentration of defensin, and that the area of the lesion is smaller on the leaves of the plant treated with higher concentration of defensin. Even though we cannot fully eradicate the infestation of the oomycete, this proved that our product worked to certain degree and can effectively guard the potatoes against the pathogen. In the future we would work further on purifying the product to optimize its function and for real life applications.


Healthy leaves on the plant traeted with lm-def.
Large area of lesions on leaves without treating the plant with lm-def

Reference

  1. Klotman, Mary E., and Theresa L. Chang. "Defensins in innate antiviral immunity." Nature Reviews Immunology 6.6 (2006): 447-456.
  2. Thevissen, Karin, Franky RG Terras, and Willem F. Broekaert. "Permeabilization of fungal membranes by plant defensins inhibits fungal growth." Applied and Environmental Microbiology 65.12 (1999): 5451-5458.
  3. de Oliveira Carvalho, André, and Valdirene Moreira Gomes. "Plant defensins—prospects for the biological functions and biotechnological properties." Peptides 30.5 (2009): 1007-1020.
  4. Solis, Julio, Giuliana Medrano, and Marc Ghislain. "Inhibitory effect of a defensin gene from the Andean crop maca (Lepidium meyenii) against Phytophthora infestans." Journal of plant physiology 164.8 (2007): 1071-1082.
  5. Levin, Aaron, et al. "Oospore formation by Phytophthora infestans in potato tubers." Phytopathology 91.6 (2001): 579-585.
  6. Fritzemeier, Karl-Heinrich, et al. "Transient induction of phenylalanine ammonia-lyase and 4-coumarate: CoA ligase mRNAs in potato leaves infected with virulent or avirulent races of Phytophthora infestans." Plant Physiology85.1 (1987): 34-41.
  7. Rohwer, Frauke, et al. "Biochemical reactions of different tissues of potato (Solanum tuberosum) to zoospores or elicitors from Phytophthora infestans."Planta 170.4 (1987): 556-561.
  8. Al-Mughrabi, Khalil I. "Control of Phytophthora infestans(the Cause of Late Blight of Potatoes) in vitro Using OxiDate." Plant Pathology Journal 4.1 (2005): 5-7.
  9. Ann, P. J., T. T. Chang, and L. L. Chern. "Mating type distribution and pathogenicity of Phytophthora infestans in Taiwan." Botanical Bulletin of Academia Sinica 39 (1998).
  10. Legard, D. E., T. Y. Lee, and W. E. Fry. "Pathogenic specialization in Phytophthora infestans: Aggressiveness on tomato." Phytopathology 85.11 (1995): 1356-1361.
  11. Medina, Marco V. "Comparison of different culture media on the mycelial growth, sporangia and oospore production of Phytophthora infestans." American journal of potato research 76.3 (1999): 121-125.
  12. De Lacy Costello, B. P. J., et al. "Gas chromatography–mass spectrometry analyses of volatile organic compounds from potato tubers inoculated with Phytophthora infestans or Fusarium coeruleum." Plant Pathology 50.4 (2001): 489-496.
  13. Tooley, P. W., et al. "Development of PCR primers from internal transcribed spacer region 2 for detection of Phytophthora species infecting potatoes."Applied and Environmental Microbiology 63.4 (1997): 1467-1475.
  14. Cohen, Yigal, Ulrich Gisi, and Thierry Niderman. "Local and systemic protection against Phytophthora infestans induced in potato and tomato plants by jasmonic acid and jasmonic methyl ester." Phytopathology 83.10 (1993): 1054-1062
Modeling

FYVE inhibition

This model was designed to investigate the competitive binding between FYVE protein domain and PI3P. Before we construct the circuit with FYVE, we have to determine whether the affinity between FYVE and PI3P is strong enough to compete with Avr3 effector protein secreted by P. infestans . We characterize this agonist-antagonist competition model by Gaddum-Schild equation. This model consists of two parts: monomeric FYVE and dimeric FYVE. According to paper research, monomeric FYVE is not so stable but the affinity of both monomeric and dimeric FYVE is quite high. The model is used to determine how many FYVE protein domain monomers we should link with a linker so that the affinity of FYVE will be high enough to compete with Avr3.

Result



These two graphs are the modeling of the competitive inhibition between FYVE domain and Avr3. The left graph shows that the monomeric FYVE can only occupy less than 10 percent of the PI3P phospholipid, but on the right graph it shows apparently that the dimeric FYVE occupies more than 80 percent of PI3P, which is a suitable competitive inhibitor that fights against the Avr3.

Conclusion

As we can see in the models dimeric FYVE is not only more stable but also have higher affinity to compete with Avr3, the effector protein secreted by P. infestans . To create FYVE protein domain with higher affinity, we can link as much FYVE domain artificially as we want. However, it takes a lot of time to add a linker protein between FYVE and connect two FYVE together. Moreover, the longer the FYVE is, the longer it takes for the plant to degrade the protein we created, which might pose a negative impact on the potato. By constructing this model, we can find out the simplest way to create a competitive inhibitor and save the time for trial and error.

Parameters

Kd monomeric FYVE Dissociation constant of monomeric FYVE 420 nM Structural Basis for Endosomal Targeting by FYVE Domains
Kd dimeric FYVE Dissociation constant of dimeric FYVE 38 nM Phosphatidylinositol 3-Phosphate Induces the Membrane Penetration of the FYVE Domains of Vps27p and Hrs
Kd Avr3 Dissociation constant of Avr3 210 nM External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effectors into Plant and Animal Host Cells

Reference

  1. Structural Basis for Endosomal Targeting by FYVE Domains
  2. Phosphatidylinositol 3-Phosphate Induces the Membrane Penetration of the FYVE Domains of Vps27p and Hrs
  3. External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effectors into Plant and Animal Host Cells

Oscillation

We construct a Lux/Aiia quorum-sensing oscillator so that S. oneidensis MR1 will generate oscillating current which can help farmers tell if the potatoes are infected. We build two models to characterize the outcome of the genetic oscillator. The first model is used to predict whether the genetic oscillator will work. Since it is possible that some of the engineered S. oneidensis inoculated on anode will not survive, the second model is used to predict whether the gene expression will oscillate when there are only a few bacteria left on the anode.

Population simulation with delay differential equation

Since our circuit design is based on the circuit published by Danino et al.[1], our first model is a slight modification of the equation from the supplementary information. This model consists of four delay differential equation. We added one more delay differential equation to this model to simulate the expression of MtrB gene.

Parameters

Description Parameter Value
CA Synthesis constant of Aiia 1
CI Synthesis constant of LuxI 4
CmtrB Synthesis constant of MtrB 1(assume)
δ Hill function constant 10-3
α Hill function constant 2500
k1 Hill function constant 0.1
τ Time delay of the production of LuxR::AHL 10
k Kinetic constant of AHL synthesis 1
b Synthesis rate of AHL 0.06
γA Enzymatic degradation rate of Aiia 15
γI Enzymatic degradation rate of LuxI 24
γH Enzymatic degradation rate of AHL 0.01
γmtrB Enzymatic degradation rate of MtrB 0.007(assume,[3])
D AHL membrane diffusion 2.5
f 0.3
g Kinetics constant of AHL degradation 0.01
d0 Maxium cell density 0.88

Conclusion

As you can see in the graph, the production of Aiia and LuxI will oscillate as the time goes by. However, the production of MtrB will build up instead of oscillating. This is because MtrB is too stable and it is not totally degraded before the production of new MtrB. Thus, it is necessary to target MtrB for proteolysis by adding a degradation tag to its coding sequence.

Single cell simulation with ODE

If we inoculate the engineered S. oneidensis MR-1 into the anode of our SMFC, chances are that some of the bacteria will not survive in soil. Thus, it is necessary to simulate if the oscillator will work with only a single bacteria or a small population of bacteria.

With a small amount of bacteria, there will not be a significant time delay, and that is why we chose a set of ODE (rather than DDE) to simulate the genetic oscillator.

With a small amount of bacteria, there will not have a significant time delay, and that’s why we chose a set of ODE to simulate the genetic oscillator.(Petros Mina, Mario di Bernardo, Nigel J. Savery, Krasimira Tsaneva-Atanasova.[1]

Conclusion

As we can see in this model, even if there is only a small amount of bacteria left on the anode, the oscillator can still work. However, the current output will be much less than that of a large population of bacteria.

Parameters

Descriptions Parameter Units Value
Basal production rate LuxI AiiA a0LI a0A μM/min μM/min 7.79x10-6 6.18x10-6
Active production rate LuxI AiiA mtrB kpLI kpLA kpmtrB μM μM μM 0.9 0.84 0.33(assume[4])
Cell reaction rate AHL production rate LuxR AHL association rate LuxR AHL dissociation rate AHL::Aiia catalytic rate AHL conc. adjustsment for environment AHL membrane diffusion constant kp2 kr1+ kr1− kcataA ηenv ηcell 1/min μM/min 1/min 1/min 1/min 1/min 16 5.99x10-5 6x10-6 7x103 3x10-5 3
Michaelis-Menten constants LuxR::AHL complex Aiia complex KmLA KmaA μM μM 1.00x10-2 1.4977x103
Degradation related parameters LuxR::AHL complex AHL τLA τAA˜ 1/min 1/min 2.40x10-2 2.76x10-3
Enzymatic degradation Inverse of KMclx Vmax × ET OT / KMclx for LuxI Vmax × ET OT / KMclx for Aiia f δ1 δ2 δ3 1/μM 1/μM.min 1/μM.min 1/μM.min 4.12x10-2 7.16x10-1 2.50x10-2 0.99x10-2 (assume[4])

Reference

  1. Danino. T, Mondragon-Palomino, O, Tsimring, L. & Hasty, J. “A synchronized quorum of genetic clocks.” Nature 463, 326-330 (2010).
  2. Petros Mina, Mario di Bernardo, Nigel J. Savery, Krasimira Tsaneva-Atanasova. “Modelling emergence of oscillations in communicating bacteria: a structured approach from one to many cells.” Published 7 November 2012
  3. Danino. T, Mondragon-Palomino, O, Tsimring, L. & Hasty, J. “A synchronized quorum of genetic clocks.” Nature 463, 326-330 (2010).

Epidemic

P. infestans is an obligate parasite, which means it cannot complete its life cycle without exploiting potatoes. However, if a potato is infected by P. infestans, the disease will spread through the potato farm within 20 days.

In this model, we try to show the difference between planting the potato we engineered and planting potato that is susceptible or moderately resistant to potato late blight. Traditionally, SEIR model will be the perfect model to characterize epidemiology. However, we made some modifications to the assumption of the traditional SEIR model so that it can reflect the reality of potato late blight epidemiology much better.

Epidemology Symbol Meaning
Susceptible The potato that is slightly or moderately resistant to P. infestans
Exposed Potatoes that are infected by a low amount of P. infestans are still curable; further spread of the disease will not occur
Infected Potatoes that are infected by a large amount of P. infestans . Potatoes infected will spread the disease and jeopardize the whole potato farm
Immunized Potatoes that has been transformed by us and is resistant against P. infestans

According to our assumptions, we developed a set of ordinary differential equations that can characterize the epidemic of potato late blight.

Parameters

SEIR MODEL(COEFFICIENTS) γ transmission rate(1/days) 0.25
α Promoter activation rate(1/days) 1.0
ε Latent rate(1/days) 0.2 British Columbia Ministry of Agriculture[3]
μ Infection rate(1/days) 0.2
k Rate of resistance to late blight 0.75 External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effectors into Plant and Animal Host Cells[2]
beta Death rate of normal potato(1/days) 1/365
SEIR MODEL(VARIABLE) S The amount of all potatoes X(1)
E The amount of suspected individual X(2) Global analysis of an SEIR model with varying population size and vaccination
I The amount of infected individual X(3) [1]
R The amount of potato that survive X(4)
V The amount of potato being immunized X(5)

Result

As we can see from the model, with our genetically modified potatoes can prevent 80% of the potatoes from being infected while only 20% of the moderately resistant potatoes can survive during the epidemic of potato late blight. Also as we can see in the graphs above, the survival rate do fit our assumption the the potatoes under the destruction of late blight only have two obvious consequences----death(once the potatoes exposed to small amount of P. infestans are seriously infected) or survival (once the “exposed potato” turned into “immunized potato”). This graphs not only show that our potatoes might be effective in fighting potato late blight in the real world but also show that potato late blight is a disease that needs to be take care of seriously and a impeccable system is necessary to fight against the disease.

Reference

  1. Chengjun Suna, Ying-Hen Hsiehc. “Global analysis of an SEIR model with varying population size and vaccination” doi:10.1016/j.apm.2009.12.005
  2. Kale SD1, Gu B, Capelluto DG, Dou D, Feldman E, Rumore A, Arredondo FD, Hanlon R, Fudal I, Rouxel T, Lawrence CB, Shan W, Tyler BM. “External Lipid PI3P Mediates Entry of Eukaryotic Pathogen Effector into Plant and Animal Host Cells” Cell. 2010 Sep 17;142(6):981-3.
  3. British Columbia Ministry of Agriculture

Soil-based-microbial-fuel-cell

Our defense system contains three different stages: prevention, detection, and cure. So how do we connect this three part together to form an impeccable defense system? First, we plant the genetically modified potatoes in the farm to prevent the disease from infecting the whole farm in a short period of time. Secondly, since no prevention strategy can be a hundred percent effective, we implement the soil based microbial fuel cell that can detect and report whether the potato is infected or not immediately. When the SMFC detects the disease, it will send an electric signal that can trigger the spraying system. The spraying system will then spray the environmentally friendly defensin that we produced automatically. Man power is not required in the whole process except planting the potatoes. Also, the defense system will also connect to a phone app that we created, so that we can know whether the potatoes are healthy or not. The defense system is localized, socialized, and mobilized. See our SMFC page for more info