Difference between revisions of "Part:BBa K2817007"

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Figure 1. AmilCP expression levels under increasing concentrations of KNO<sub>3</sub>.  
 
Figure 1. AmilCP expression levels under increasing concentrations of KNO<sub>3</sub>.  

Revision as of 08:48, 14 October 2019

PyeaR-RBS-amilCP

The promoter PyeaR is sensitive to nitrate and nitrite. When nitrate and nitrite enter E. coli, they are converted to nitric oxide. Nitric oxide binds to the repressor protein NsrR, which inactivates PyeaR to inhibit transcription of downstream genes. Then the promoter PyeaR will be activated to express the amilCP.

1. Usage and Biology

We learnt that iGEM 2010 Team BCCS-Bristol had used BBa_K381001 to detect the soil nitrate and nitrite to demonstrate the fertility of soil. Thus, farmers can determine which soils are fertilized by detecting the fluorescence of GFP reporter gene. In this way, farmers only need to apply fertilizer in places where there is no fertility, which can save excess fertilizer. Given the economic costs and the impact of eutrophication on ecosystems, the use of BBa_K381001 has great benefits for both agriculture and the environment. However, due to the influence of outdoor temperature, GFP fluorescence density was fluctuated significantly. This instability is unfavorable for the detection of soil fertility. In addition, the detection of GFP fluorescence signal requires special equipment that is not readily available for farmers. Therefore, we replaced GFP with blue chromoprotein (amilCP encoded protein) for visual detection. On the one hand, amilCP expression is less affected by temperature and is a more stable reporter than GFP. On the other hand, blue chromoprotein can be visualized by human eyes, instead of requiring the special equipment. Therefore, we believe that our improved part BBa_K2817007 is very beneficial to farmers.

2. Characterization

According to the results of the ShanghaiTechChina_B 2016 team, 100μM Sodium Nitroprusside Dihydrate (SNP) aqueous solution can continually release NO, and the NO concentration is stable at about 5.5μM. Since our project also tested for inflammatory signals, we chose this concentration before testing for BBa_K381001 and BBa_K2817007.

The construction of BBa_K381001 can be seen from Figure 1A. We transformed the plasmid containing BBa_K381001 into DH5α competent E. coli strain and cultured at 37 ℃ overnight to dilute to OD600 = 0.4. Then we took half of bacteria as control and the rest was added SNP aqueous solution, and induced at 37 ℃ for 6 h. Then the fluorescence intensity of cells was observed under microplate reader (Figure 1B) and fluorescence inverted microscope (Figure 1C). The histogram of GFP fluorescent density and microscope images indicated that PyeaR could effectively activated by NO and there was almost no leakage expression.

T--NEU_China_A--improve-1.png

Figure 1. The test of BBa_K381001. A, the construction of BBa_K381001. B, Histogram of GFP fluorescence: LB control, without SNP, with 100μM SNP. C, GFP fluorescence image from top to bottom: without SNP, with 100μM SNP.

The construction of BBa_K2817007 can be seen from Figure 2A. We transformed the plasmid containing BBa_K2817007 into DH5α, and cultured at 37 ℃ overnight to dilute to OD = 0.4. Then we took half as control and the other half added SNP aqueous solution and induced at 37 ℃ for 6 h. We also set up negative control group which doesn’t contain amilCP. After 6 h at 37 ℃, 1 mL of the bacterial solution was centrifuged at 8,000 r.p.m for 1 min (Figure 2B). We could directly observe the result of PyeaR being activated by NO without special equipment.

T--NEU_China_A--improve-2.png

Figure 2. The test of BBa_K2817007. A, the construction of BBa_K2817007. B, Pellets of bacteria transformed with plasmid containing BBa_K2817007 after induction of 6h. From left to right: negative control group, without SNP group, with 100μM SNP group.

3. Conclusion

In conclusion, we confirmed our improvement through an experimental comparison between the two parts. In the real world, our improved part BBa_K2817007 has better usability. In the future, we will further confirm the situation of different concentrations of NO and different temperature conditions.

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]


UPNAvarra_Spain 2019, Improve the Characterization of BBa_K2817007

BBa_K2817007 was first designed by Zhaoyu Liu from team NEU_China_A in iGEM 2018. It is a nitrate reporter, PyeaR-amilCP composite. This team only test the BioBrick’s sensitivity at 100 μM sodium nitroprusside dihydrate aqueous solution, confirming the expression of the blue chromoprotein under those conditions. However, we have better characterized the expression of amilCP under a concentration gradient of potassium nitrate.

We transformed the plasmid containing BBa_K2817007 (our twin BBa_K3287000) into E. coli competent cells and cultured at 37ºC until OD = 0.4. Then we add KNO3 al different concentrations to induce the expression at 37 ℃ for 6 hours. After that, 2 mL of the bacterial culture were centrifuged at 3,000 r.p.m for 3 minutes, so we could observe at first sight the result of PyeaR promoter being activated by nitrate (Figure 1).



T--UPNAvarra_Spain--BronzeFigure1.png

<img src="T--UPNAvarra_Spain--BronzeFigure1.png" width=630px height=450px/>


Figure 1. AmilCP expression levels under increasing concentrations of KNO3.

For these experimental results we generated a mathematical model, in order to prove that the (imaging) data we have gathered in the lab is in fact learnable by a simple regression model. We have opted out by a standard Least-Square error (linear) regression model, which has been run on the dataset obtained in the imaging part. This dataset consists of the average RGB color in the colored part of the pellets used at different concentrations of KNO3 (Figure 2A). For each color, we have subselected the channels that we are interest for the problem. That is the Red channel in this case. It can be seen how the data is easily learnable by a linear regression model (Figure 2B) and, moreover, the error in the model training is rather small (0.18).



<img src="T--UPNAvarra_Spain--BronzeFigure2.png" width=630px height=450px/>

Figure 2. Modeling a Nitrate biosensor. A) Input data; B) Regression model.