Difference between revisions of "Part:BBa K2599017"

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The goal of our system is to regulate the soil microbiota in order to reach the maximum crop productivity. To accurately predict the curcumin content from NPK content in soil, we create a bio-sensor. This sensor can precisely detect the curcumin containment in turmeric. After the detection of curcumin, results can be fitted into our productivity model and utilize artificial intelligent to increase the accuracy.
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Our system manipulates soil microbiota in order to deliver maximum crop productivity. Our test subjects are turmeric plants, from which we want to extract curcumin. To accurately predict the curcumin content from nitrogen (N), phosphorus (P), and potassium (K) content in soil, we create a biosensor that precisely detects the curcumin concentration in turmeric. After the detection of curcumin, results can be fitted into our productivity model with artificial intelligent to increase the accuracy. Linking our productivity model to a curcumin transformation model allows us to perfectly predict the crop productivity and maintain balance of soil microbiota.
With the cooperation of productivity model and curcumin transformation model, we can perfectly predict the crop productivity and maintain balance soil microbiota.
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===Curcumin===
 
===Curcumin===
  
Curcumin is a natural lipid-soluble yellow compound from the plant tumeric. It is a potent antioxidant as well as antitumorigenic and anti- inflammatory molecule. Although curcumin has been proved its therapeutic efficacy against many human ailments, but the problem is it is hard to absorb by human cells. To solve this problem, a paper has discovered a curcumin carrier protein called αS1-casein, shows high binding affinity with curcumin. We then utilize this property of αS1-casein to create a curcumin bio-sensor.
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Curcumin is a natural lipid-soluble yellow compound from the plant turmeric. It is a potent antioxidant as well as anti-tumorigenic and anti- inflammatory molecule. Although curcumin has been proved to be therapeutic to many human ailments, it is hard for human cells to absorb. We discovered in literature that to solve this problem, a curcumin carrier protein called αS1-casein, was proven to have high binding affinity with curcumin. We then utilize this property of αS1-casein to create a curcumin biosensor.
  
  
 
===αS1-casein===
 
===αS1-casein===
  
Casiens are proteins commonly found in mammalian milk and is a mixture of four phosphoprotein. One of the phosphoprotein is αS1-casein, which contains no disulfide bonds and relatively little tertiary structure. As their primary function is nutritional, binding large amounts of calcium, zinc and other biologically important metals, amino acid substitutions or deletions have little impact on function.  
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Caseins are proteins commonly found in mammalian milk and is a mixture of four phosphoproteins. One of the phosphoprotein is αS1-casein, which contains no disulfide bonds and relatively little tertiary structure. As their primary function is nutritional, binding large amounts of calcium, zinc and other biologically important metals, amino acid substitutions or deletions have little impact on function.
  
  
 
===The Binding Between Curcumin and αS1-casein===
 
===The Binding Between Curcumin and αS1-casein===
  
According to the reference, we found how this two bind together.
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According to the reference, curcumin has a β-diketone moiety, flanked by two phenolic groups, that helps bind to proteins through hydrophobic interactions.
 +
The carboxyl-terminal of αS1-casein (100−199 residues) predominantly contains hydrophobic amino acids, which may be involved in the binding process. Residues 14−24 in αS1-casein are hydrophobic in nature and form a surface “patch” of hydrophobicity. Curcumin may probably be binding at these two sites, with two different ranges of affinity through hydrophobic interaction. One with high affinity [(2.01 ± 0.6) × 106 M−1] and the other with low affinity [(6.3 ± 0.4) × 104 M−1].
  
Curcumin has a β-diketone moiety, flanked by two phenolic groups, that helps bind to proteins through hydrophobic interactions.
 
 
The carboxyl-terminal of αS1-casein (100−199 residues) predominantly contains hydrophobic amino acids, which may be involved in the binding process. Residues 14−24 in αS1-casein are hydrophobic in nature and form a surface “patch” of hydrophobicity. Curcumin may probably be binding at these two sites, with two different ranges of affinity through hydrophobic interaction. One with high affinity [(2.01 ± 0.6) × 106 M−1] and the other with low affinity [(6.3 ± 0.4) × 104 M−1].
 
  
  
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<b>1. Determine Standard Curve and Create the Formula</b>
 
<b>1. Determine Standard Curve and Create the Formula</b>
  
We used the standard samples of curcumin and diluted it in dilution buffer. Next, we detected the diluted curcumin samples by curcumin biosensor and made the standard curve. Since it was the logarithmic function, we put the curcumin concentration into the natural logarithm, and did the polynomial curve fitting. We obtained the result in Figure 5, R2 =0.9995. This represented the prediction of real samples from the following formula was really close to real value.
+
We used the standard samples of curcumin and diluted it in dilution buffer. Next, we detected the diluted curcumin samples by curcumin biosensor and made the standard curve. Since it was a logarithmic function, we put the curcumin concentration into the natural logarithm, and did the polynomial curve fitting. We obtained the result in Figure 5, R2 =0.9995. This represented the prediction of real samples from the following formula was really close to real value.
  
  
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<b>2. The Detection Result of Real Samples from Turmeric Root</b>
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<b>2. The Detection Result of Real Samples from Turmeric Rhizome</b>
  
Our final goal is to predict the concentration of curcumin in real samples by the biosensor. Therefore, we prepared the most curcumin content part, the root of turmeric to pretest our biosensor. First of all we milled the turmeric root and divided the powder into two groups. One of them was added with extraction buffer but not underwent the extraction protocol, and the other was added with extraction buffer but underwent the extraction process. The result (Fig. 8) showed only the sample which underwent the extraction process was able to be detected, which represented our sensor had strong specificity to curcumin. Moreover, our curcumin biosensor would not be disturbed even if taking the whole turmeric content to detect.
+
Our final goal is to predict the concentration of curcumin in real samples by the biosensor. Therefore, we prepared the most curcumin content part, the rhizome of turmeric to pretest our biosensor. First of all we milled the turmeric rhizome and divided the powder into two groups. One of them was added with extraction buffer but not underwent the extraction protocol, and the other was added with extraction buffer but underwent the extraction process. The result (Fig. 8) showed only the sample which underwent the extraction process was able to be detected, which represented our sensor had strong specificity to curcumin. Moreover, our curcumin biosensor would not be disturbed even if taking the whole turmeric content to detect.
  
  
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<b>3. Create a new method to detect curcumin in time</b>
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In order to feedback our productivity model in time and make it more accurately, we want to find a way to detect curcumin instantly. We supposed that turmeric can also detect curcumin as well as the rhizome and other parts of turmeric.
 +
According to Plant Science, photosynthesis aids in the production of curcumin [3], so we selected the leaves as the test samples and detected it by DPV. Our sample divided into five groups: negative control, normal leaves, and turmeric leaves in three different areas. After estimating the turmeric concentration using the formula obtained above, we get the result of the figure below (fig 6). From this result, we can see that negative control, and normal leave can’t detect curcumin, and area 1, 2 & 3 detected curcumin. It means that we can use turmeric leaves to detect curcumin instead of using curcumin rhizome, and each sample of the curcumin concentration is different. From this result, we can reasonably speculate that the curcumin concentration of the turmeric rhizome is relative with the curcumin concentration of turmeric leaves. Next, we must start a large number of experiments with curcumin in the same turmeric leaves and rhizome to create a new model to improve the feedback system of the entire productivity model. This is what we want to achieve in the future.
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{{#tag:html|<img style="width: 95%" src="https://static.igem.org/mediawiki/2018/7/70/T--NCTU_Formosa--1.png" alt="" />}}
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===Conclusion===
 
===Conclusion===
  
In our conclusion, we use the electrochemistry method, DPV, to prove that we can detect curcumin if we use the gold chips to connect with αS1-casein as biosensor. We also make the standard curve and create accurate formula to support the prediction of curcumin concentration in real samples. Finally we certify the specificity is perfect in real samples. According to these experiments, we successfully create a new curcumin biosensor. Because of it, we can detect curcumin in time and feedback to our model.
+
In our conclusion, we use the electrochemistry method, DPV, to prove that we can detect curcumin if we use the gold chips to connect with αS1-casein as biosensor. We also created a standard curve and generated an accurate formula to support the prediction of curcumin concentration in real samples. Moreover, we certify the specificity is perfect in real samples. Finally, we successfully detected curcumin in turmeric leaves. Based on these experiments, we created a new curcumin biosensor. In order to improve our productivity model, we designed a new method to detect curcumin concentration quickly and consistently. Our biobrick and device allow for calibration of our productivity model to better demonstrate the efficacy of our soil regulation system. Click here to see how our applied system improves turmeric plants on our farm!
  
  

Revision as of 21:33, 17 October 2018


T7 Promoter+RBS+GS linker+αS1-casein

NCTU_Formosa 2018 designed a Biobrick contains αS1-casein [http://2014.igem.org/Team:SF_Bay_Area_DIYbio/Parts#Alpha-s1_casein_.28CSN1S1.29] and a GS linker (BBa_K1974030) ahead as a Curcumin biosensor.



Figure 1. Composite part of αS1-casein


Our system manipulates soil microbiota in order to deliver maximum crop productivity. Our test subjects are turmeric plants, from which we want to extract curcumin. To accurately predict the curcumin content from nitrogen (N), phosphorus (P), and potassium (K) content in soil, we create a biosensor that precisely detects the curcumin concentration in turmeric. After the detection of curcumin, results can be fitted into our productivity model with artificial intelligent to increase the accuracy. Linking our productivity model to a curcumin transformation model allows us to perfectly predict the crop productivity and maintain balance of soil microbiota.


Introduction

Curcumin

Curcumin is a natural lipid-soluble yellow compound from the plant turmeric. It is a potent antioxidant as well as anti-tumorigenic and anti- inflammatory molecule. Although curcumin has been proved to be therapeutic to many human ailments, it is hard for human cells to absorb. We discovered in literature that to solve this problem, a curcumin carrier protein called αS1-casein, was proven to have high binding affinity with curcumin. We then utilize this property of αS1-casein to create a curcumin biosensor.


αS1-casein

Caseins are proteins commonly found in mammalian milk and is a mixture of four phosphoproteins. One of the phosphoprotein is αS1-casein, which contains no disulfide bonds and relatively little tertiary structure. As their primary function is nutritional, binding large amounts of calcium, zinc and other biologically important metals, amino acid substitutions or deletions have little impact on function.


The Binding Between Curcumin and αS1-casein

According to the reference, curcumin has a β-diketone moiety, flanked by two phenolic groups, that helps bind to proteins through hydrophobic interactions. The carboxyl-terminal of αS1-casein (100−199 residues) predominantly contains hydrophobic amino acids, which may be involved in the binding process. Residues 14−24 in αS1-casein are hydrophobic in nature and form a surface “patch” of hydrophobicity. Curcumin may probably be binding at these two sites, with two different ranges of affinity through hydrophobic interaction. One with high affinity [(2.01 ± 0.6) × 106 M−1] and the other with low affinity [(6.3 ± 0.4) × 104 M−1].



Establishment of Curcumin Biosensor

Cloning of αS1-casein

We got the amino acid sequence of αS1-casein from NCBI, and adjust the DNA sequence to optimize its expression in E. coli. We also added a GS linker ahead to enhance the function of sensor and synthesized the gblock fragment from IDT. First of all of cloning process, we did PCR to acquire the product of GS Linker-αS1 casein DNA fragment. (Fig. 2) Next, we digested the fragment and ligated it to pet30a vector. Finally, we transformed the plasmid with GS Linker-αS1 casein to E. coli. BL21 DE3 and made protein expression.



Figure 2. PCR product of αS1-casein


Chip Production

αS1-casein Modification to Gold Chip

1. Dip the gold chips in 10mM Mua, RT for 4hrs.

2. Wash the chips with 95% EtOH three times and dry.

3. Add EDC+NHS mixture (100+100mM in DDW) on chips, RT for 1hrs.

4. DDW rinse the chips and dry.

5. Add αS1-casein on chips, RT for 1hrs.

6. Wash with PBS three times and dry.

6. Dip the chips in blocking solution, RT for 1.5hrs.

7. Wash with PBS three times and dry.


Detection Method of Curcumin Biosensor

Electrochemistry Introduction

After choosing αS1-casein as our biosensor, we should choose a method to detect curcumin. We choose the Differential Pulse Voltammetry (DPV) method.


Differential Pulse Voltammetry

DPV uses the difference between before and after the pulse application in order to solve the influence of background noise. This principle is difference from EIS. We hope we can find out which method is more sensitive to curcumin, and create more accurate formula.


Measurement protocol of Curcumin Biosensor

1. Add the diluted curcumin samples on our biosensor to react for 30min.

2. Rinse with wash buffer and dry the chips.

3. Wash the reference and counter electrodes with DDW, and dry them.

4. Set up the three electrodes system within electrochemical cell. (Fig. 3, left)

5. Use the prototype of electrochemical machine to measure the DPV method. (Fig.3, right)


Figure 3. Three electrode system (left) and prototype of electrochemical machine (right)


Electrochemical Measurement Result

Pretest of Differential Pulse Voltammetry (DPV)

First of all, we used DPV to check whether our biosensor can detect curcumin. As we mentioned above, DPV method tested the current change when curcumin binding. Therefore, we compared the two kinds of chips, the red line was the general chips, and the blue line was the chips modified with αS1-casein (Fig. 4). As long as our biosensor contacted with the standard samples of curcumin (from Sigma Aldrich), its current value would become larger. We can easily observed that our biosensor with αS1-casein produced more fierce Redox reaction than another. Figure 4 also represented that the biosensor modified with αS1-casein have more effect of detecting curcumin than none.


Figure 4.The sensitivity test of curcumin biosensor in Dpv. (Reduction)


Application of Curcumin Biosensor to Detect Real Samples

1. Determine Standard Curve and Create the Formula

We used the standard samples of curcumin and diluted it in dilution buffer. Next, we detected the diluted curcumin samples by curcumin biosensor and made the standard curve. Since it was a logarithmic function, we put the curcumin concentration into the natural logarithm, and did the polynomial curve fitting. We obtained the result in Figure 5, R2 =0.9995. This represented the prediction of real samples from the following formula was really close to real value.


X = Curcumin Concentration; Y = DPV Peak Current



Figure 5.The standard curve and the concentration formula of curcumin.


2. The Detection Result of Real Samples from Turmeric Rhizome

Our final goal is to predict the concentration of curcumin in real samples by the biosensor. Therefore, we prepared the most curcumin content part, the rhizome of turmeric to pretest our biosensor. First of all we milled the turmeric rhizome and divided the powder into two groups. One of them was added with extraction buffer but not underwent the extraction protocol, and the other was added with extraction buffer but underwent the extraction process. The result (Fig. 8) showed only the sample which underwent the extraction process was able to be detected, which represented our sensor had strong specificity to curcumin. Moreover, our curcumin biosensor would not be disturbed even if taking the whole turmeric content to detect.


Figure 6.Detection of real samples from Turmeric


3. Create a new method to detect curcumin in time


In order to feedback our productivity model in time and make it more accurately, we want to find a way to detect curcumin instantly. We supposed that turmeric can also detect curcumin as well as the rhizome and other parts of turmeric. According to Plant Science, photosynthesis aids in the production of curcumin [3], so we selected the leaves as the test samples and detected it by DPV. Our sample divided into five groups: negative control, normal leaves, and turmeric leaves in three different areas. After estimating the turmeric concentration using the formula obtained above, we get the result of the figure below (fig 6). From this result, we can see that negative control, and normal leave can’t detect curcumin, and area 1, 2 & 3 detected curcumin. It means that we can use turmeric leaves to detect curcumin instead of using curcumin rhizome, and each sample of the curcumin concentration is different. From this result, we can reasonably speculate that the curcumin concentration of the turmeric rhizome is relative with the curcumin concentration of turmeric leaves. Next, we must start a large number of experiments with curcumin in the same turmeric leaves and rhizome to create a new model to improve the feedback system of the entire productivity model. This is what we want to achieve in the future.




Conclusion

In our conclusion, we use the electrochemistry method, DPV, to prove that we can detect curcumin if we use the gold chips to connect with αS1-casein as biosensor. We also created a standard curve and generated an accurate formula to support the prediction of curcumin concentration in real samples. Moreover, we certify the specificity is perfect in real samples. Finally, we successfully detected curcumin in turmeric leaves. Based on these experiments, we created a new curcumin biosensor. In order to improve our productivity model, we designed a new method to detect curcumin concentration quickly and consistently. Our biobrick and device allow for calibration of our productivity model to better demonstrate the efficacy of our soil regulation system. Click here to see how our applied system improves turmeric plants on our farm!



Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]


Reference

1. Gupta, S. C., et al. (2012). "Discovery of curcumin, a component of golden spice, and its miraculous biological activities." Clin Exp Pharmacol Physiol 39(3): 283-299.

2. Le Parc, A., et al. (2010). "α(S1)-casein, which is essential for efficient ER-to-Golgi casein transport, is also present in a tightly membrane-associated form." BMC Cell Biology 11: 65-65.

3. Sneharani, A. H., et al. (2009). "Interaction of αS1-Casein with Curcumin and Its Biological Implications." Journal of Agricultural and Food Chemistry 57(21): 10386-10391.

4. Teresa Treweek (September 12th 2012). Alpha-Casein as a Molecular Chaperone, Milk Protein Walter L. Hurley, IntechOpen, DOI: 10.5772/48348.

5.Palazon, F.; Montenegro Benavides, C.; Léonard, D.; Souteyrand, É.; Chevolot, Y.; Cloarec, J. P. Carbodiimide/NHS derivatization of COOH-terminated SAMs: activation or byproduct formation?. Langmuir, 2014, 30, 4545-4550.