Difference between revisions of "Part:BBa K2947001"

Revision as of 06:51, 6 October 2022

UGT33

UDP-glucose-4-O-glucosyltransferase(T8GT) protein coding region.UGT33 is the most active gene among R. rosea Tyrosol-Modifying UGTs. The protein it encodes reduces tyrosol to produce salidroside. UGT33 is used as the last gene to complete salidroside biosynthesis from tyrosine.

Usage and Biology

The UGT super-family is one of the largest enzyme families in the plant kingdom. Previous research has shown that(Michel et al., 2017) among all the genes encoding UDP-glucose-4-O-glucosyltransferase(T8GT), UGT33 is the most active T8GT.

When 2019 GDSYZX is trying to transfer UGT33 to vector, the transfer of UGT33 into carrier was not smooth during the experiment. We started with the traditional enzyme training but it didn't work. Then we switched to a ligase, and it still was a false positive. Finally, we used the recombination link approach and successfully transferred UGT33 into the plasmid.

The protoplast and salidroside standard samples of arabidopsis thaliana transfected with UGT33 and 4HPAAS genes were compared by liquid chromatography. The peak value of the product measured in sample No. 1 was basically consistent with the peak value of salidroside standard samples.

Therefore, we can determine that the transfected protoplast of arabidopsis thaliana can produce salidroside.

MODELING(with 4HPAAS:BBa_K2947000)

By Using Image J software to analyze the relative brightness of protein bands, we obtained the data of gray value of two genes/4h varying with time. There is a positive correlation between gray value / 4H and protein expression, so we use the standardized gray value / 4H to reflect protein expression. Then we use SPSS software to draw the scatter plot of gray value / 4H about time to judge the overall trend. Among them, we changed the SPSS output format to English. (The relative gray value is equal to the following gray value divided by the gray value of 4h)

We found that these scatters showed a quadratic function parabola trend, so we square the time data. Next, we test Pearson correlation coefficient of time, time squared and gray value / 4H to check whether there is a linear correlation between them.

Then we found that the correlation between UGT33 gene variables was quite high, P value was less than 0.05, with a high significance. The correlation coefficient of 4HPAAS gene was higher than 0.6, P value was higher than 0.05, but less than 0.2. Although the degree was not significant, the correlation coefficient was higher. It can be concluded that the binary linear regression equation can be constructed between the relative gray value of two gene sequences and the square term of time . By using the least square method, we get the regression equation between relative gray value and time.

Relative gray value = beta0 + beta1 * time + beta2 * time squared

Then we analyze the fitting degree of the two models and calculate the R-square of the regression equation. We find that the goodness of fit of the two models is good, and the R-square is close to 1. Therefore, both models are better. In the case of significance level 0.05, the F-test of the two models is significant, which shows that all independent variable time has a higher linear significance for relative gray value on the whole, and the p-value of coefficient T test is less than 0.05. It also shows that each independent variable time has a higher linear significance for relative gray value.

The image is analyzed: the point is the gray value data measured in our experiment divided by the gray value of 4 hours, and the curve is the image of regression equation calculated by least square method. In the images of UGT33 and 4HPAAS, we can see that the monotonic trend of the curve model is basically the same. Because the relative gray value basically reflects the amount of protein expression, with the increase of time, it can be considered that the amount of protein expression increases correspondingly, but the speed decreases gradually. This can be explained by the reduction of reactants. Since we don't have many time points, we can think that when time reaches 9-12 minutes, the amount of expression reaches its maximum. Even the largest data points are above the curve in both images. Then the relative gray value decreases with the increase of time. Based on this modeling analysis, we think that 12 minutes is enough for the experiment.

Sequence and Features