Team:NCTU Formosa/Improve

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NCTU_Formosa: Project Improvement
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:

  1. Datasets
  2. Results and the candidates we suggested

     The way we use SCM to cure fungal diseases is just a part of 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 1: The dataset of the insecticidal protein

     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

Figure 1: The score distribution of positive and negative(insecticidal and non-insecticidal) dataset

Figure 2: The ROC curve of the model

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.

Table 2: Proteins that predicted insecticidal

NEW PART:
- fMt with a constitutive promoter

Introduction

      fMt is a metallothionein that has high affinity with As(III). 2009 Groningen iGEM used it to purify the polluted water. However, 2009 Groningen iGEM ligased it with a low constitutive promotor, BBa_J23109. We failed serval times when and tried to ligased these two parts. After sequencing its plasmid, we found out there was two SpeI site in the plasmid. So we decided to replaced J23109 with J23119 and conducted a series of experiments to ensure the function.

Modifying and Improving the Existing Biobrick

1. Previous biobrick: BBa_K190031 of 2009 iGEM Groningen

    The new part: BBa_K2262015

     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 3.) Figure 4 shows the electrophoresis of BBa_K190019 when its plasmid was cut by XbaI and PstI and Figure 5 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 6.)

Figure 3: The sequence of J23109 Plasmid.

Figure 4: The electrophoresis of BBa_K190019.

Figure 5: The electrophoresis of BBa_J23119 and BBa_J23109.

Figure 6: 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. Table 3 shows the experimental design for the growth curve of E. coli DH5α and the result shows in Figure 7.

Table 3: The experiment design for the growth curve of E. coli DH5α.



Figure 7: 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 4 shows the experimental design for the growth curve of E. coli DH5α and the result shows in Figure 8 .

Table 4: The experiment design for the growth curve of E. coli DH5α with fMt plasmid.




Figure 8: 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

     BBa_J33201 is a promoter containing the E. coli JM109 chromosomal arsenic detoxification operon (ars operon). Both 2013 Buenos Aires iGEM team and 2006 Edinburgh iGEM team used it to detect arsenic ions in the water. Buenos Aires team ligased it with GFP and Edinburgh team ligased it with LacZ α. The two teams also submitted biobricks, BBa_K1106004 and BBa_J33203. We wanted to know whether these two biobricks have more application, such as detecting the arsenic pollution in Chinese medicine. We also wanted to know whether they can detect other metal ions. Several experiments were done to evaluate the function of this promoter.

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 Figure 9.

Table 5: The experimental design for the growth curve of E. coli DH5α.

Figure 9: 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 8 shows the experimental design for the expression of GFP and the result shown in Figure 10.

Table 6: 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 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 7 shows the experimental design for the expression of GFP and the result shown in Figure 11.

Table 7: The experimental design the expression of GFP. The excitation peak is 485nm, the emission peak is 538nm, and the auto cutoff is 515nm.

Figure 11: 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 7 shows the experimental design for the growth curve of E. coli DH5α and the result shows in Figure 12.

Table 7: The experimental design the the growth curve of E. coli DH5α.

Figure 12: 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: detecting 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 8 shows the experimental design for detecting arsenic ion and the result shows in Figure 13.

Table 8: The experimental design for detecting arsenic ion.

Figure 13: 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 9 shows the experimental design for detecting arsenic ion and the result shows in Figure 14.

Table 9: The experimental design for detecting arsenic ion.

Figure 14: 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.

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