Difference between revisions of "Team:UMaryland/TPAvPCA"

 
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<div class="titleRegion" style="background-image: url(https://static.igem.org/mediawiki/2018/7/7b/T--UMaryland--PTNT4.png)">
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<div class="titleRegion" style="background-image: url(https://static.igem.org/mediawiki/2018/d/d0/T--UMaryland--PTNT5.png)">
 
<div class="container" style="height: 200px;">
 
<div class="container" style="height: 200px;">
 
<div class="titleContainer">
 
<div class="titleContainer">
<div class="titleText">Bacterial Cellulose</div>
+
<div class="titleText">TPA vs PCA Detection</div>
<div class="subtitleText">Does the addition of a cellulose binding domain accelerate the degradation of PET?</div>
+
<div class="subtitleText">a consideration of response time and sensitivity</div>
 
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<div class="meatyRegion">
 
<div class="meatyRegion">
 
<div id="Overview">
 
<div id="Overview">
                                        <div class="meatSubtitle">
 
Model Setup
 
                                        </div>
 
 
<div class="meatMeat">
 
<div class="meatMeat">
When we ran into the issue of detection of the degradation of PET, we found two potential sensor systems that we would be able to use, one which detected protocatechuate (PCA) and the other detecting terephthalate (TPA). To determine which sensor system was better for our needs, we used the MatLab SimBiology package to model the degradation of PET down to PCA and further to the cellular metabolite. The SimBiology files are available at this link:  
+
We found two potential biosensor systems that we could possibly use for detection of the degradation of PET, one which detected protocatechuate (PCA) and the other detecting terephthalate (TPA). To determine which sensor system was better for our needs, we used the MatLab SimBiology package to model the degradation of PET down to PCA and further to the cellular metabolite. The SimBiology files are available at this link:  
 
</div>
 
</div>
 
<div class="meatMeat">
 
<div class="meatMeat">
 
      <a href="https://static.igem.org/mediawiki/2018/0/0d/T--UMaryland--simbiomodels.zip"><u>Simbiology Zip File for TPA vs PCA</u></a>
 
      <a href="https://static.igem.org/mediawiki/2018/0/0d/T--UMaryland--simbiomodels.zip"><u>Simbiology Zip File for TPA vs PCA</u></a>
 
 
 +
</div>
 +
                                        <div class="meatSubtitle">
 +
Simulation Setup
 +
                                        </div>
 +
                                        <div class="meatMeat">
 +
There are four major processes that occur in our sensor setup as shown below:
 
</div>
 
</div>
 
<div class="center">
 
<div class="center">
 
<div class="imageBox">
 
<div class="imageBox">
<img src="https://static.igem.org/mediawiki/2018/9/95/T--UMaryland--BCsandwich.png" style="max-width: 100%" alt="Waluigi Time!">
+
<img src="https://static.igem.org/mediawiki/2018/1/1f/T--UMaryland--modelcartoon.png" style="max-width: 100%" alt="Waluigi Time!">
<div class="imageBoxDescription">Bacterial cellulose creates a "sandwich" to maximize PETase contact with the surface</div>
+
<div class="imageBoxDescription"></div>
 
</div>
 
</div>
 
</div>
 
</div>
 
<div class="meatMeat">
 
<div class="meatMeat">
We determined the surface area of a PET bottle modeling it as a perfect cylinder with diameter d = 7 cm, height h = 23 cm, and mass m = 4 g. which is about the average size of a disposable water bottle.
+
which are the depolymerization of PET into TPA, the degradation of TPA to PCA, the transport of PCA and TPA, and the expression of GFP from the activation of transcription factors. The parameters of the TPA and PCA sensor systems are described below:
 
</div>
 
</div>
<div>
+
<div class="imageBox">
Surface area of bottle = 2dh = (2)(7π cm)(23 cm) = 322π cm²
+
<img src="https://static.igem.org/mediawiki/2018/6/68/T--UMaryland--TPAvPCAtable.png" style="max-width: 100%" alt="Waluigi Time!">
</div>
+
<div class="imageBoxDescription"></div>
<div>
+
Volume of PET = wdh = (0.1 cm)(7π cm)(23 cm) = 16.1π cm3
+
 
</div>
 
</div>
 +
     
 
<div class="meatMeat">
 
<div class="meatMeat">
We want to model the exact difference between the local and the global concentrations in such a scenario. In order to do this, we used an assumed value for the “working thickness” of cellulose, which is the thickness at which making the cellulose layer thicker will yield a small increase in the insulating behavior of the cellulose layers. We used a value that is an underestimate of 0.1 mm as our starting value. We aim to use experimental data to adjust this value. We determined the following parameters:  
+
Using SimBiology we created two sensor systems for our degradation analysis:
 
</div>
 
</div>
<div>
+
<div class="imageBox">
Cellulose density = 1.5 g/cm3
+
<img src="https://static.igem.org/mediawiki/2018/9/9e/T--UMaryland--Simbiochart.png" style="max-width: 100%" alt="Waluigi Time!">
</div>
+
<div class="imageBoxDescription"></div>
<div>
+
Total working weight of cellulose = (1.5 g/cm3)(322π cm²)(0.01 cm) = 15.87  g cellulose
+
 
</div>
 
</div>
 +
 
<div class="meatMeat">
 
<div class="meatMeat">
Using values obtained from the literature about the CipA-CBD [1] where our CBD was acquired from, we determined:
+
Through an extensive literature search, we found the enzyme kinetics parameters and protein concentrations as listed below. We converted all values of Vmax and kcat in units of M product / (s * M protein). We used non-reversible Michaelis-Menten as our enzyme kinetics parameters except the degradation of PETase, which we approximated using our zero order enzyme kinetics equation <a href="https://2018.igem.org/Team:UMaryland/BCmodel"><u>described here</u></a> and the transport of TPA and PCA by TpiAB and PcaK respectively, which followed a law of mass action kinetics.
 
</div>
 
</div>
<div>
+
Maximum binding of CipA-CBD to cellulose = 0.43 µM/g cellulose
+
<div class="imageBox">
 +
<img src="https://static.igem.org/mediawiki/2018/c/c1/T--UMaryland--modeltable.png" style="max-width: 100%" alt="Waluigi Time!">
 +
<div class="imageBoxDescription"><a href="https://static.igem.org/mediawiki/2018/e/e7/T--UMaryland--simbioparameters.xlsx"><u>Excel file with links</u></a></div>
 
</div>
 
</div>
<div>
+
<div class="meatMeat">
Local concentration of PETase at 50% binding = (0.5)(15.87 g)(0.43 µM/g) = 3.41 µM PETase
+
We ran the simulation with the following parameters:
 
</div>
 
</div>
<div class="meatSubtitle">
 
Enzyme Kinetics
 
 
<div class="meatMeat">
 
<div class="meatMeat">
The degradation of PET follows heterogeneous catalysis, where the reactants and the products [2] are in different phases. This creates difficulty in modeling the rate of degradation but teams such as Tianjin China has used Langmuir's Equation, secretion, and diffusion models to model the process of secretion, diffusion, adsorption, and degradation. However, while their models gave great insight into the mathematical relationships between these variables, it was not possible to obtain practical results as many of the variables used in the equation are not found in the literature and are difficult to obtain experimentally.
+
- “600 uM” of PET added in solution
 
</div>
 
</div>
 
<div class="meatMeat">
 
<div class="meatMeat">
We have developed the “working cellulose” model which is more practical in predicting the rate of degradation when PETase with a cellulose binding domain is added to a degradation system. We modeled the rate of degradation of PETase as a zero-order system, where the concentration of the reactant is negligible to the rate of the reaction. This is present in many surface-limited chemical catalysis models, where the catalyst surface is saturated by the reactants, and the rate limiting step can be simplified in the surface area of the catalyst.
+
- Constant enzyme concentrations
 
</div>
 
</div>
 
<div class="meatMeat">
 
<div class="meatMeat">
However, in our PET degradation model, the reverse holds true, where the concentration of PETase is the rate limiting variable compared to the surface area of the PET film when considering practical examples such as the degradation of a plastic bottle. We determined the following rate equation for the degradation of PET:
+
- Reversibility of reactions not considered
 
</div>
 
</div>
<div class="imageBox">
+
<div class="meatMeat">
<img src="https://static.igem.org/mediawiki/2018/a/a9/T--UMaryland--model1.png" style="max-width: 100%" alt="Waluigi Time!">
+
- Simulated rich oxygenated media with excess NADPH
<div class="imageBoxDescription">Equations from the assumption of zero order reaction</div>
+
 
</div>
 
</div>
 
<div class="meatMeat">
 
<div class="meatMeat">
In our reduced and simplified zero order enzyme kinetics equation, we only need to know the product, time, and enzyme concentrations to determine the kinetic variable k to predict degradation. Through values obtained through the original PETase discovery paper by Yoshida et al, we were able to calculate the kinetic variable for both low-crystalline and high-crystalline PET. 
+
- Fixed 50 uM of transcription factor concentrations (a gross exaggeration)
 
</div>
 
</div>
 
<div class="meatMeat">
 
<div class="meatMeat">
At 30°C, pH 7.0, [PETase concentration] = 50nM, time = 18 hrs: PETase produced a total of 0.015 mM of product when fed high-crystalline PET and 0.3 mM of product when fed thin-sheet PET.
+
- 30 Day simulation
 
</div>
 
</div>
                                         <div class="meatMeat">
+
<div class="meatSubtitle">
Using above data we can determine the rate constants.
+
Simulation Results
 
+
                                        </div>
 +
                                         <div class="center">
 +
                                                <div class="imageBox">
 +
    <img src="https://static.igem.org/mediawiki/2018/4/46/T--UMaryland--simresult1.png" style="max-width: 100%" alt="Waluigi Time!">
 +
    <div class="imageBoxDescription"></div>
 +
</div>
 
</div>
 
</div>
<div class="imageBox">
+
<div class="meatMeat">
<img src="https://static.igem.org/mediawiki/2018/4/40/T--UMaryland--consts.png" style="max-width: 100%" alt="Waluigi Time!">
+
In the PCA detection system, we predict that there are high levels of PCA in the media, but the rate limiting reaction in the degradation pathway is the conversion of DCD to PCA. Transport of PCA limits the activation of the PcaU. The model predicts that concentrations of PCA will exceed the solubility limit (120 uM in water), but we see that there is full activation of PcaU prior to the solubility limit being reached. There is rapid activation of PcaU at about 1.5 days.
<div class="imageBoxDescription">Equations from the assumption of zero order reaction</div>
+
 
</div>
 
</div>
<div class="meatSubtitle">
+
                                        <div class="center">
Results
+
  <div class="imageBox">
 +
<img src="https://static.igem.org/mediawiki/2018/2/2f/T--UMaryland--simresult2.png" style="max-width: 100%" alt="Waluigi Time!">
 +
<div class="imageBoxDescription"></div>
 +
        </div>
 
</div>
 
</div>
 
<div class="meatMeat">
 
<div class="meatMeat">
Therefore, with the calculated local concentration of our enzyme and our enzyme kinetic parameter we were able to predict the rate of degradation of a plastic water bottle. If we desire 4 grams of PET to completely degrade to its subsequent product MHET, using the molecular weight of MHET (Mw = 210.185 g mol-1), we determine that the final concentration of MHET in a 1 L container would be 0.019 M. Since PET is not an aqueous substance, it is difficult to assign it a “concentration” value for our equation. Therefore we would determine the creation time of MHET rather than the degradation time of PET.  
+
In the TPA detection system we see an immediate increase in the concentration of TPA in the media, with a similar problem of the rate of transport limiting the activation of TphC. In this simulation, we see an increase in the time to full response to about 7 days, which is about 4x longer than the PCA based system. However, in this simulation we exceed the solubility limit of TPA (90 uM) rapidly, therefore we tweaked the model to a fixed concentration 90 uM concentration of TPA in the media.
 
</div>
 
</div>
<div class="meatMeat">
+
                                        <div class="center">
Using our equation:
+
  <div class="imageBox">
</div>
+
<img src="https://static.igem.org/mediawiki/2018/b/bb/T--UMaryland--simresult3.png" style="max-width: 100%" alt="Waluigi Time!">
<div class="imageBox">
+
<div class="imageBoxDescription"></div>
<img src="https://static.igem.org/mediawiki/2018/3/31/T--UMaryland--bceq.png" style="max-width: 100%" alt="Waluigi Time!">
+
  </div>
 
</div>
 
</div>
 
<div class="meatMeat">
 
<div class="meatMeat">
With the following parameters:
+
In the fixed TPA concentration model, we see an even longer increase in the time to full response to almost 30 days, which is now 20x slower compared to the PCA based system. Therefore we concluded that for fastest response time to degradation of PET, a PCA based sensor system would be much better than a TPA based sensor system.
 
</div>
 
</div>
 
<div class="meatMeat">
 
<div class="meatMeat">
[PETase] = 3.41 µM
+
With regards to sensitivity, we were unable to get a clear picture from our literature search. We searched for lacZ activity which is downstream of the PCA or TPA activated promoter system. There are conflicting units of specific activity (U) and Miller Units, which cannot be converted between each other. However, we found that the detection limit of PCA is close to 1 uM while the detection of TPA is close to 10 uM [8,10]. Therefore we concluded that the sensitivity of the PCA sensor is also better than the TPA based sensor. </div>
 
</div>
 
</div>
 +
<div class="meatSubtitle">
 +
Simulation Conclusions
 +
                                        </div>
 
<div class="meatMeat">
 
<div class="meatMeat">
- k = 0.00463 s-1 for high crystalline PET
+
- The biochemistry literature is of limited help when trying to acquire values
 +
</div>
 +
<div class="meatMeat">
 +
- PCA Detection System provides better response time and sensitivity vs.the TPA Detection System
 +
</div>
 +
<div class="meatMeat">
 +
- Transport across the membrane the limiting factor in both scenarios
 +
</div>
 +
<div class="meatMeat">
 +
- Increasing the expression of transport proteins the biggest challenge
 +
</div>
 +
<div class="meatMeat">
 +
- PcaK transport system is much faster and smaller than the TpiBA system
 +
</div>
 +
<div class="meatMeat">
 +
- Metabolism of PCA negligible in limiting response of PcaU system
 
</div>
 
</div>
<div class="meatMeat">
+
<div>
-  [MHET] = 0.019 M
+
1. Sabathé, F. & Soucaille, P. Characterization of the CipA scaffolding protein and in vivo production of a minicellulosome in Clostridium acetobutylicum. J. Bacteriol. 185, 1092–1096 (2003).
 
</div>
 
</div>
<div class="meatMeat">
+
<div>
Solving for time, we predict that complete degradation of one PET bottle sandwiched between two bacterial cellulose sheets with 50% saturation of our PETase-CBD fusion protein will occur in 1.2 x 106 seconds, or approximately 14 days.  
+
2. Yoshida, S. et al. A bacterium that degrades and assimilates poly(ethylene terephthalate). Science 351, 1196–1199 (2016).
 
</div>
 
</div>
<div class="meatMeat">
+
<div>
As degradation progresses, the appropriateness of our zero-order enzyme kinetics model will decrease as the surface area of PET available to our enzyme will become more significant. Therefore, a time to 50% degradation will be more indicative of the actual degradation of PET. We determined this value to be about 7 days.
+
3. Fukuhara, Y., Kasai, D., Katayama, Y., Fukuda, M. & Masai, E. Enzymatic properties of terephthalate 1,2-dioxygenase of Comamonas sp. strain E6. Biosci. Biotechnol. Biochem. 72, 2335–2341 (2008). </div>
 +
<div>
 +
4. Salier, E., Laue, H., Schläfli Oppenberg, H. R. & Cook, A. M. Purification and some properties of (1R,2S)-1,2-dihydroxy-3,5-cyclohexadiene-1,4-dicarboxylate dehydrogenase from Comamonas testosteroni T-2. FEMS Microbiol. Lett. 130, 97–102 (1995).
 
</div>
 
</div>
</div>
+
<div>
<div class="meatMeat">
+
5. Fujisawa, H. & Hayaishi, O. Protocatechuate 3,4-dioxygenase. I. Crystallization and characterization. J. Biol. Chem. 243, 2673–2681 (1968).
1: Sabathé, F. & Soucaille, P. Characterization of the CipA scaffolding protein and in vivo production of a minicellulosome in Clostridium acetobutylicum. J. Bacteriol. 185, 1092–1096 (2003).
+
 
</div>
 
</div>
<div class="meatMeat">
+
<div>
2: Yoshida, S. et al. A bacterium that degrades and assimilates poly(ethylene terephthalate). Science 351, 1196–1199 (2016).
+
6. Nichols, N. N. & Harwood, C. S. PcaK, a high-affinity permease for the aromatic compounds 4-hydroxybenzoate and protocatechuate from Pseudomonas putida. J. Bacteriol. 179, 5056–5061 (1997).
 
</div>
 
</div>
 +
<div>
 +
6. Nichols, N. N. & Harwood, C. S. PcaK, a high-affinity permease for the aromatic compounds 4-hydroxybenzoate and protocatechuate from Pseudomonas putida. J. Bacteriol. 179, 5056–5061 (1997).
 +
</div>
 +
<div>
 +
7. Hosaka, M. et al. Novel tripartite aromatic acid transporter essential for terephthalate uptake in Comamonas sp. strain E6. Appl. Environ. Microbiol. 79, 6148–6155 (2013).
 +
</div>
 +
<div>
 +
8. Siehler, S. Y., Dal, S., Fischer, R., Patz, P. & Gerischer, U. Multiple-level regulation of genes for protocatechuate degradation in Acinetobacter baylyi includes cross-regulation. Appl. Environ. Microbiol. 73, 232–242 (2007).
 +
</div>
 +
<div>
 +
9. Trautwein, G. & Gerischer, U. Effects exerted by transcriptional regulator PcaU from Acinetobacter sp. strain ADP1. J. Bacteriol. 183, 873–881 (2001).
 +
</div>
 +
<div>
 +
10. Kasai, D., Kitajima, M., Fukuda, M. & Masai, E. Transcriptional regulation of the terephthalate catabolism operon in Comamonas sp. strain E6. Appl. Environ. Microbiol. 76, 6047–6055 (2010).
 +
</div>
 +
</div>
 
</div>
 
</div>
 
</div>
 
</div>

Latest revision as of 02:04, 18 October 2018

Template Title Template Title

TPA vs PCA Detection
a consideration of response time and sensitivity
We found two potential biosensor systems that we could possibly use for detection of the degradation of PET, one which detected protocatechuate (PCA) and the other detecting terephthalate (TPA). To determine which sensor system was better for our needs, we used the MatLab SimBiology package to model the degradation of PET down to PCA and further to the cellular metabolite. The SimBiology files are available at this link:
Simulation Setup
There are four major processes that occur in our sensor setup as shown below:
Waluigi Time!
which are the depolymerization of PET into TPA, the degradation of TPA to PCA, the transport of PCA and TPA, and the expression of GFP from the activation of transcription factors. The parameters of the TPA and PCA sensor systems are described below:
Waluigi Time!
Using SimBiology we created two sensor systems for our degradation analysis:
Waluigi Time!
Through an extensive literature search, we found the enzyme kinetics parameters and protein concentrations as listed below. We converted all values of Vmax and kcat in units of M product / (s * M protein). We used non-reversible Michaelis-Menten as our enzyme kinetics parameters except the degradation of PETase, which we approximated using our zero order enzyme kinetics equation described here and the transport of TPA and PCA by TpiAB and PcaK respectively, which followed a law of mass action kinetics.
We ran the simulation with the following parameters:
- “600 uM” of PET added in solution
- Constant enzyme concentrations
- Reversibility of reactions not considered
- Simulated rich oxygenated media with excess NADPH
- Fixed 50 uM of transcription factor concentrations (a gross exaggeration)
- 30 Day simulation
Simulation Results
Waluigi Time!
In the PCA detection system, we predict that there are high levels of PCA in the media, but the rate limiting reaction in the degradation pathway is the conversion of DCD to PCA. Transport of PCA limits the activation of the PcaU. The model predicts that concentrations of PCA will exceed the solubility limit (120 uM in water), but we see that there is full activation of PcaU prior to the solubility limit being reached. There is rapid activation of PcaU at about 1.5 days.
Waluigi Time!
In the TPA detection system we see an immediate increase in the concentration of TPA in the media, with a similar problem of the rate of transport limiting the activation of TphC. In this simulation, we see an increase in the time to full response to about 7 days, which is about 4x longer than the PCA based system. However, in this simulation we exceed the solubility limit of TPA (90 uM) rapidly, therefore we tweaked the model to a fixed concentration 90 uM concentration of TPA in the media.
Waluigi Time!
In the fixed TPA concentration model, we see an even longer increase in the time to full response to almost 30 days, which is now 20x slower compared to the PCA based system. Therefore we concluded that for fastest response time to degradation of PET, a PCA based sensor system would be much better than a TPA based sensor system.
With regards to sensitivity, we were unable to get a clear picture from our literature search. We searched for lacZ activity which is downstream of the PCA or TPA activated promoter system. There are conflicting units of specific activity (U) and Miller Units, which cannot be converted between each other. However, we found that the detection limit of PCA is close to 1 uM while the detection of TPA is close to 10 uM [8,10]. Therefore we concluded that the sensitivity of the PCA sensor is also better than the TPA based sensor.
Simulation Conclusions
- The biochemistry literature is of limited help when trying to acquire values
- PCA Detection System provides better response time and sensitivity vs.the TPA Detection System
- Transport across the membrane the limiting factor in both scenarios
- Increasing the expression of transport proteins the biggest challenge
- PcaK transport system is much faster and smaller than the TpiBA system
- Metabolism of PCA negligible in limiting response of PcaU system
1. Sabathé, F. & Soucaille, P. Characterization of the CipA scaffolding protein and in vivo production of a minicellulosome in Clostridium acetobutylicum. J. Bacteriol. 185, 1092–1096 (2003).
2. Yoshida, S. et al. A bacterium that degrades and assimilates poly(ethylene terephthalate). Science 351, 1196–1199 (2016).
3. Fukuhara, Y., Kasai, D., Katayama, Y., Fukuda, M. & Masai, E. Enzymatic properties of terephthalate 1,2-dioxygenase of Comamonas sp. strain E6. Biosci. Biotechnol. Biochem. 72, 2335–2341 (2008).
4. Salier, E., Laue, H., Schläfli Oppenberg, H. R. & Cook, A. M. Purification and some properties of (1R,2S)-1,2-dihydroxy-3,5-cyclohexadiene-1,4-dicarboxylate dehydrogenase from Comamonas testosteroni T-2. FEMS Microbiol. Lett. 130, 97–102 (1995).
5. Fujisawa, H. & Hayaishi, O. Protocatechuate 3,4-dioxygenase. I. Crystallization and characterization. J. Biol. Chem. 243, 2673–2681 (1968).
6. Nichols, N. N. & Harwood, C. S. PcaK, a high-affinity permease for the aromatic compounds 4-hydroxybenzoate and protocatechuate from Pseudomonas putida. J. Bacteriol. 179, 5056–5061 (1997).
6. Nichols, N. N. & Harwood, C. S. PcaK, a high-affinity permease for the aromatic compounds 4-hydroxybenzoate and protocatechuate from Pseudomonas putida. J. Bacteriol. 179, 5056–5061 (1997).
7. Hosaka, M. et al. Novel tripartite aromatic acid transporter essential for terephthalate uptake in Comamonas sp. strain E6. Appl. Environ. Microbiol. 79, 6148–6155 (2013).
8. Siehler, S. Y., Dal, S., Fischer, R., Patz, P. & Gerischer, U. Multiple-level regulation of genes for protocatechuate degradation in Acinetobacter baylyi includes cross-regulation. Appl. Environ. Microbiol. 73, 232–242 (2007).
9. Trautwein, G. & Gerischer, U. Effects exerted by transcriptional regulator PcaU from Acinetobacter sp. strain ADP1. J. Bacteriol. 183, 873–881 (2001).
10. Kasai, D., Kitajima, M., Fukuda, M. & Masai, E. Transcriptional regulation of the terephthalate catabolism operon in Comamonas sp. strain E6. Appl. Environ. Microbiol. 76, 6047–6055 (2010).

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