Difference between revisions of "Part:BBa K2599017"

Revision as of 12:50, 15 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.

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. With the cooperation of productivity model and curcumin transformation model, we can perfectly predict the crop productivity and maintain balance soil microbiota.

Introduction

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.

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.

The Binding Between Curcumin and αS1-casein

According to the reference, we found how this two bind together.

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].

Experiment

Cloning

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.

After the digestion and ligation with pet30a backbone. We expression the protein in E.coli BL21 DE3.

Chip Production

Modification protocol

1. Dipped the chips in 10mM Mua, RT 4hrs. Wash with 95% EtOH three times.

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

3. Add αS1-casein on chips, RT 1hrs, wash with PBS three times.

4. Dipped the chips in blocking solution, RT 1.5hrs, wash with PBS three times.

Electrochemistry

Introduction

After choosingαS1-casein as our biosensor, we should choose a method to detect curcumin. We choose the electrochemical impedance spectroscopy (EIS) and Differential Pulse Voltammetry (DPV), the two detecting methods in electrochemistry.

Electrochemical Impedance Spectroscopy

EIS is simple, convenient, and rapid, so that it is quite suitable for detecting whether the biosensor effective or not. This method uses the principle that impedance will be changed by charges transfer, and then measure the change of impedance by Alternate Current. Therefore, if we find out that our measured result has the stronger change than negative control, we can say our method could produce a fierce Redox reaction. However, because EIS is not a suitable method for detecting small molecule, the error will be very large if we choose it to perform formula computing. So we decide to use DPV to improve this problem.

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

1. Dilute Curcumin in PBS/PB buffer, react 30min

2. Rinse with PSB/PB buffer on time and dry the chip.

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

4. Set up the three electrode with electrochemical cell

5. Measure electrochemistry.

Data Analysis

1. Electrochemical Impedance Spectroscopy (EIS)

First of all, we used EIS to check whether our biosensor can detect curcumin. As we mentioned above, EIS method tested the impedance change when charges transfered. Therefore, we compared the two samples, the red line was connected to general chip, and the blue line was connected with αS1-casein (Fig. 4). As long as our biosensor connected to curcumin , its impedance 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 connected with αS1-casein have more effect of detecting curcumin than none.

2. Differential Pulse Voltammetry (DPV)

After proving that adding αS1-casein can detect curcumin, we hoped to use more accurate method to show our data. We chose DPV method to replicate the experiment above, and determined which method was more sensitive. Comparing with Fig 4 and Fig 5, we found that chips with αS1-caseinis showed almost no changes in EIS method, but changed a lot in DPV method. In this way, we can explain DPV method was more sensitive for detection and showed better signal than EIS.

3. Improvement (DPV)

After knowing DPV is more sensitive, we want to know in which condition is more suitable to detect curcumin. We tried two buffers, PB and PBS, as curcumin solvent. In Fig 6, blue line was detection in PB buffer, and red line was in PBS buffer. In the result, LOD (limit of detection) of curcumin in PB buffer was 10pM and in PBS buffer was 1nM. This meaned that the sensitivity by using PB buffer was one hundred times more than using PBS buffer. We also confirmed that NaCl would increase background noise in our method. In the conclusion, we would chose the PB buffer as our solvent rather than the PBS buffer.

Application

1. Determine Standard Curve from Detection of Curcumin in PB Buffer

We knew that connecting our biosensor with αS1-casein and detecting curcumin in PB buffer is the best condition to determine curcumin concentration by DPV. We used the data in this condition to fit 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 7, R² =0.9995. This represented the prediction from the following formula was really close to real value.

2. The Detection Result of Real Samples from Turmeric

Our final goal is to predict the concentration of curcumin by the bio-sensor. Therefore, we milled turmeric and divided into two groups to detect. One of them was nonextracted, and the other was extracted, both of them were added with water. The sample which didn’t be extracted was unable to be detected, which is because curcumin uaually existed in cell. Furthermore, this result represented our sensor had strong specificity to curcumin. That is, our sensor would not be disturbed even if taking the whole turmeric to detect.

Conclusion

In our conclusion, we use EIS and DVP, two different kinds of electrochemistry method, to prove that we can detect curcumin if we use chips to connect with alphaS1-casein as bio-sensor. We also find the better condition to detect curcumin and create accurate formula to support our bio-sensor. Finally we certify the specificity is perfect. According to these experiments, we successfully create a new curcumin bio-sensor. Because of it, we can detect curcumin in time and feedback to our model.

Sequence and Features