Difference between revisions of "Team:NCTU Formosa/Improve"
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| − | <h1 style="margin-top:100px;">The Expression of LacZ α<span><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_J33203">(BBa_J33203)</a></span></h1> | + | <h1 style="margin-top:100px;">The Expression of LacZ α <span><a href="http://parts.igem.org/wiki/index.php?title=Part:BBa_J33203">(BBa_J33203)</a></span></h1> |
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1. Experimental Design: Growth curve of GFP biosensor in arsenic solutions of different concentration | 1. Experimental Design: Growth curve of GFP biosensor in arsenic solutions of different concentration | ||
Revision as of 17:49, 1 November 2017
Improvement - Finding more pest-resistant candidates for former NCTU_Formosa
- Using the same method SCM to build pest-resistant peptide prediction system
To improve the project of NCTU_Formosa 2016, we applied SCM to make an insecticidal peptide prediction system, using a quicker way to search for their target peptides and leaving them a group of potential target peptides.
Content:
- Datasets
- Results and the candidates we suggested
The way we use SCM to cure fungal diseases is just a part for its ability. In fact, the peptide prediction system based on the SCM can be specialized in different cases of evaluating sequences.
We decided to apply the method to NCTU_Formosa 2016, which utilized spider toxin to kill the pests. We introduced the scoring card to the insecticidal protein to see whether we could also predict invertebrate proteins from ion channel impairing toxins, improving their searching tool while finding more candidates for the project last year.
First, we collected the insecticidal and ion channel impairing toxins by 2016 selection database. After deleting peptides which contained non-standard amino acids, we randomly chose positive and negative data to our datasets and divided them into two datasets, training datasets and testing datasets.
Table:
For training parts, after initializing the first scorecard, we used IGA to optimize the scorecard for ten generations.
Results
FullTrain_acc=91.70454568181819 CV acc(train)=93.8636343698348 CV auc(train)=95.44599143143164 Best theshold=498.75 Best_acc(test)=88.86363681818182 Sensitivity(test)=0.7031249936523439 Specitivity(test)=0.9202127637222726
Discussion
To improve the project of NCTU_Formosa 2016, we introduced the scoring card method to the insecticidal proteins. By using the method, we can predict more new insecticidal proteins.
We collected about three thousands of ion channel impairing toxins.
Below is the excerpt of the peptide list.
一個表格
NEW PART:
- fMt with a constitutive promoter
Introduction
This sequence is designed for constitutively chelating arsenic ions.
We ligated a constitutive promoter(BBa_J23119) with metallothionein (fMt, BBa_K190019) to produce arsenic-binding protein. This metallothionein (fMt) is a kind of chelating protein from Fucus vesiculosus. It can bind both Arsenite (III) and Arsenate(V). This part was first designed by Groningen of iGEM 2009. The part of K190019 consists of RBS and fMt.
Modifying and Improving the Existing Biobrick
1. Previous biobrick: BBa_K190031 of 2009 iGEM Groningen
The metallothionein (BBa_K190031) is a fMt(BBa_K190019) under control of a low constitutive promotor (BBa_J23109). We failed several times in replicating the ligation of these two parts. After sequencing these three plasmids, we found BBa_J23109 has two Spel restriction sites in the prefix.(Figure 2.) The Figure 3 shows the electrophoresis of BBa_K190019 when its plasmid was cut by XbaI and PstI and the Figure 4 shows the electrophoresis of BBa_J23119 and BBa_J23109 when their plasmids were cut by SpeI and PstI. Thus, we decided to modify the biobrick by ligating fMt(BBa_K190019) with another constitutive promoter, BBa_J23119.(Figure 5.)
Figure 2: The sequence of J23109 Plasmid.
Figure 3: The electrophoresis of BBa_K190019.
Figure 4: The electrophoresis of BBa_J23119 and BBa_J23109.
Figure 5: The new biobrick design.
Results
We first examined the growth curve of E. coli DH5α in arsenic solution and compared the growth curve of E. coli DH5α in arsenic solution with that curve in solution without arsenic ions. The Table 1 shows the experimental design for the growth curve of E. coli DH5α and the result shows in Table 2.
Table 1: The experiment design for the growth curve of E. coli DH5α.
Figure 6: The growth of E.coli DH5α in different conditions.
We find that E. coli DH5α won’t be affected by arsenic concentration below 100ppm.
Then we conducted the next experiment. We examined the growth curve of E. coli DH5α in arsenic solution and compared the growth curve of E. coli DH5α in arsenic solution with that curve in solution with no arsenic ions. Table 3 shows the experimental design for the growth curve of E. coli DH5α and the result shows in Table 4 .
Table 2: The experiment design for the growth curve of E. coli DH5α with fMt plasmid.
Figure 7: The growth of E. coli DH5α in different conditions.
The results of this experiment indicate that E. coli DH5α containing the transformed plasmid can survive in arsenic concentrations from 1 ppm to 100 ppm.
In conclusion, we modified the part of BBa_K190031 by replacing the promoter BBa_J23109 by BBa_J23119. The growth of E. coli with this new plasmid us not affected the arsenic concentration.
Increase Function:
- Application and Specificity
Introduction
The project was to examine the arsenic pollution in Chinese medicine. We identified two biobricks with ars promoter, BBa_K1106004, and BBa_J33203. We wanted to know whether these biobricks can use in Chinese medicine and whether they have sensitive to other ions.
The Expression of GFP (BBa_K1106004)
1. Experimental Design: Growth curve of GFP biosensor in arsenic solutions of different concentration
This experiment was to test the growth of E. coli DH5α with the arsenic solution in comparison to the solution without arsenic ions. Table 5 shows the experimental design for the growth curve of E. coli DH5α and the result shows in Table 6.
Table 3: The experimental design for the growth curve of E. coli DH5α.
Figure 8: The growth of E. coli DH5α in different conditions.
The result shows that the growth of E. coli GFP biosensor in solutions was not affected by the arsenic ion, so GFP biosensor can be used to test arsenic ions.
2. Experimental Design: Function test on detecting arsenic ion in Chinese medicine
This experiment was to quantify the arsenic concentration in three kinds of Chinese medicine: Scutellaria baicale, Angelica, and Yanjing, based on the expression of GFP in arsenic solution. We compared the expression of GFP in E. coli DH5α with plasmid BBa_K1106004 in three kinds of Chinese medicine solutions with the expression in the arsenic solution of 1 and 10 ppm. Table 7 shows the experimental design for the expression of GFP and the result shown in Table 8.
Table 4: The experimental design the expression of GFP. The excitation peak is 485nm, the emission peak is 538nm, and the auto cutoff is 515nm.
Figure 9: The expression of GFP.
The result shows that the growth of E. coli GFP biosensor in solutions was not affected by the arsenic ion, so GFP biosensor can be used to test arsenic ions in Chinese medicine.
3. Experimental Design: Specificity
This experiment was to test the growth of E. coli DH5α with the arsenic solution in comparison to solution without arsenic ions.
This experiment was to test whether the GFP biosensor was responsive to other ions. We compared the expression of GFP in solutions of Copper ions and Lead ion. Table 9 shows the experimental design for the expression of GFP and the result shown in Table 10.
Table 5: The experimental design the expression of GFP. The excitation peak is 485nm, the emission peak is 538nm, and the auto cutoff is 515nm.
Figure 10: The expression of GFP.
The Expression of LacZ α (BBa_J33203)
1. Experimental Design: Growth curve of GFP biosensor in arsenic solutions of different concentration
This experiment was to test the growth of E. coli DH5α with the arsenic solution in comparison to the solution without arsenic ions. Table 11 shows the experimental design for the growth curve of E. coli DH5α and the result shows in Table 12.
Table 6: The experimental design the the growth curve of E. coli DH5α.
Figure 11: The experimental design the the growth curve of E. coli DH5α.
The growth of E. coli DH5α with BBa_J33203 plasmid was not affected by the arsenic solution of different concentration.
2. Experimental Design: GDetecting arsenic ion in arsenic solution
This experiment was to test the function of Lacz biosensor. We compared the blue color in the arsenic solution of different concentration. Table 13 shows the experimental design for detecting arsenic ion and the result shows in Figure 5.
Table 7: The experimental design for detecting arsenic ion.
Figure 12: The result of detecting arsenic ion.
The results show that the density of blue color is associated with the concentration of the arsenic solution.
3. Experimental Design: Specificity of LacZ α biosensor
This experiment was to test whether LacZ α biosensor was sensitive to copper and lead ions. We compare the blue color in three types of 10 ppm solution. Table 14 shows the experimental design for detecting arsenic ion and the result shows in Figure 6.
Table 8: The experimental design for detecting arsenic ion.
Figure 13: The pictures show LacZ α biosensor is not responsive to copper ions and lead ions.
The results of our experiment show that the LacZ α biosensor can grow normally in the arsenic solution of different concentration. This LacZ α biosensor detects arsenic ion in arsenic solution and with good quality of specificity. In a word, this LacZ α biosensor can be an effective fast screening instrument for detecting the arsenic pollution.
