Difference between revisions of "Part:BBa K3431007"

(Characterization using invertase)
 
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<partinfo>BBa_K3431007 short</partinfo>
 
<partinfo>BBa_K3431007 short</partinfo>
  
This toehold switch has been designed to open up its hairpin loop structure upon binding with miRNA-31, resulting in the translation of downstream reporter protein. The design of toehold switch can be separated into the following 5 regions from its 5' end: trigger binding sites, stem region, loop region with RBS, complimentary stem region with start codon, and linker amino acids. In our constructions of toehold switches for miRNA-31, we optimise the toehold switch structure by altering their loop region and linker sequence. We incorporate two designs of the loop region from two articles: the original work on toehold switch (Green, A.A. et al., 2014) and the adaptation of toehold switch to detect zika virus (Pardee, K. et al., 2016). Pardee, K. et al. have truncated the loop structure from 19 base pairs in the original work conducted by Green, A.A. et al. to 12 base pairs in order to reduce the leakage of output expression. Hence we hope to observe an increase in the output's dynamic range by implementing the loop sequence utilised by Pardee, K. et al.. As for our selection on the linker sequences, we choose to test out the linker sequence from Pardee, K. et al. and a random linker sequence which we generated in order to minimize the free energy of toehold switch mRNA secondary structure.  
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=== Description ===
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zr31 toehold switch is a regulatory part for the downstream gene. With this part, the protein expression can be controlled by the miR-31, which is a biomarker for oral squamous cell carcinoma (OSCC)<sup>[1][2][3][4]</sup>.
 +
The sequence of the toehold switch can be divided into 5 regions: a trigger binding site (TBS), a stem region, a loop region containing ribosome binding site (RBS), another complimentary stem region with a start codon, and a linker.
 +
When the miR-31 binds with the TBS, the hairpin structure of the toehold can be opened up and the ribosomes can bind with the RBS, triggering the translation of the downstream reporter.
 +
zr31 Toehold Switch can be combined with different kinds of reporters, and further applied to oral cancer detection.<br><br>
  
For this particular toehold switch (zr31), we incorporate the loop structure from Pardee, K. et al. and the random linker structure generated by our team.  
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===Design & Model===
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The design of the toehold switch was mainly based on previous research<sup>[5][6][7][8][9][10]</sup>. For the zr31 toehold switch, we adopted the loop structure from Green et al., 2016<sup>[11]</sup>, and a random linker structure we designed. Using NUPACK analysis and Vienna binding models, we designed the sequence of the toehold switch. (See our model page: https://2020.igem.org/Team:CSMU_Taiwan/Model )
  
NUPACK ANALYSIS <br>
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<html>
https://static.igem.org/mediawiki/parts/5/51/T--CSMU_Taiwan--zr31_NU.png
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<br>
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<figure style="mirgin-right: 1em; float:left; width:40%; border:1px solid black">
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<img src="https://static.igem.org/mediawiki/parts/5/51/T--CSMU_Taiwan--zr31_NU.png" style="display: block;margin-left: auto;margin-right: auto; width: 70%">
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<figcaption style="text-align: center;">
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Figure 1. NUPACK analysis result
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</figcaption>
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</figure>
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</div>
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<figure style="mirgin-right: 1em; float:left; width:40%; border:1px solid black">
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<img src="https://static.igem.org/mediawiki/parts/4/43/T--CSMU_Taiwan--zr31_Ve.png" style="display: block;margin-left: auto;margin-right: auto; width: 100%">
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<figcaption style="text-align: center;">
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Figure. 2. ViennaRNA Package result
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</figcaption>
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</figure>
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</html>
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<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
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NUPACK analysis suggested the MFE (minimum free energy) RNA structure at 37℃, whose free energy was -22.00 kcal/mol. As for the Vienna binding, the black line in the figure indicates the amount of energy required to open the secondary structures of the TBS. The line red indicates the amount of energy required to open the secondary structure after the binding of the trigger. As a result, this indicates that zr31 will successfully be bound and open the locked structure.
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<br><br>
  
VIENNA RNA PACKAGE  <br>
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===Characterization using invertase===
https://static.igem.org/mediawiki/parts/5/51/T--CSMU_Taiwan--zr31_NU.png
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Link to our model page: https://2020.igem.org/Team:CSMU_Taiwan/Model
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The 2020 iGEM CSMU-Taiwan characterized the zr31 toehold switch with T7 promoter (BBa_I719005), invertase reporter protein (BBa_K3431000), and T7 terminator(BBa_K731721). We built up a composite part BBa_K3431023 to test its functionality.
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The plasmids were transcribed and translated with the PURExpress® In Vitro Protein Synthesis Kit (New England Biolabs) at 37℃ for 2 hours. We would then add 5μl of 0.5M sucrose, and measured the glucose concentration with Bionime Rightest™ GM550 glucose meter after 30 minutes of enzymatic reaction time.
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In our experiments, the ON state refers to the conditions with miRNA triggers; while the OFF state means that there was no miRNA in the environment. We calculated the ON/OFF ratio of the toehold switch, which is defined as “the glucose concentration of the ON state/ the glucose concentration of the OFF state”.
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<html>
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<br>
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<div style="width=100%; display:flex; align-items: center; justify-content: center">
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<img src="https://static.igem.org/mediawiki/parts/3/3d/T--CSMU_Taiwan--zr31_%28BBa_K3431023%29.png" style="width:40%">
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<figcaption style="text-align: center;">
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</figcaption>
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</div>
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Fig. 3. The glucose productions of the zr31 toehold switch-regulated invertase in different states. The blue bar refers to the OFF state (not added with miRNA). The green bar refers to the ON state (added with miR-31 trigger). The yellow bar refers to the state with non-related RNAs (added with miR-191). The pink bar refers to the state with non-related RNAs (added with miR-223).
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<br>
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</html>
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<b>Results</b><br>
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The glucose concentration in the ON state with miR-31 is 310.67 mg/dL, indicating the high sensitivity of the toehold switch. The ON/OFF ratio with miR-31 is 2.65, which suggested the regulatory function of the toehold switch. By contrast, in the experiment of negative selection, the ON/OFF ratios with miR-191 and miR-223 are 1.46 and 1.21, respectively. These ratios are close to 1, meaning the zr31 toehold switch has high specificity. As a result, the zr31 toehold switch has been proven to be useful for miR-31 detection.
 +
<br><br>
 +
 
 +
===Characterization using invertase===
 +
To understand the correlation of the trigger amount and the glucose production, we added different amounts of miR-31 to the protein synthesis kit and produced the proteins at 37℃ for 2 hours. We would then add 5μl of 0.5M sucrose and measured the glucose concentration with the glucose meter after 30 minutes.
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<html>
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<br>
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<div style="width=100%; display:flex; align-items: center; justify-content: center">
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<img src="https://static.igem.org/mediawiki/parts/b/b7/T--CSMU_Taiwan--EXP_5_zr31_.png" style="width:50%">
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</div>
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Fig. 4. Glucose production under different amounts of miR-31.
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<br>
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</html>
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<b>Results</b><br>
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As shown above, the glucose concentration rose as the miR-31 triggers increased, representing a positive correlation.
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<br><br>
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===References===
 +
1. Liu, C.-J., Lin, S.-C., Yang, C.-C., Cheng, H.-W., & Chang, K.-W. (2011). Exploiting salivary miR-31 as a clinical biomarker of oral squamous cell carcinoma. Head & Neck, 34(2), 219–224. https://doi.org/10.1002/hed.21713<br>
 +
 
 +
2. Mazumder, S., Datta, S., Ray, J. G., Chaudhuri, K., & Chatterjee, R. (2019). Liquid biopsy: miRNA as a potential biomarker in oral cancer. Cancer epidemiology, 58, 137–145. https://doi.org/10.1016/j.canep.2018.12.008<br>
 +
 
 +
3. Min, A., Zhu, C., Peng, S., Rajthala, S., Costea, D. E., & Sapkota, D. (2015). MicroRNAs as Important Players and Biomarkers in Oral Carcinogenesis. BioMed Research International, 2015, 1–10. https://doi.org/10.1155/2015/186904<br>
 +
 
 +
4. Momen-Heravi, F., Trachtenberg, A. J., Kuo, W. P., & Cheng, Y. S. (2014). Genomewide Study of Salivary MicroRNAs for Detection of Oral Cancer. Journal of Dental Research, 93(7_suppl), 86S-93S. https://doi.org/10.1177/0022034514531018<br>
 +
 
 +
5. Green, A. A., Silver, P. A., Collins, J. J., & Yin, P. (2014). Toehold switches: de-novo-designed regulators of gene expression. Cell, 159(4), 925–939. https://doi.org/10.1016/j.cell.2014.10.002<br>
 +
 
 +
6. Green, A. A., Kim, J., Ma, D., Silver, P. A., Collins, J. J., & Yin, P. (2017). Complex cellular logic computation using ribocomputing devices. Nature, 548(7665), 117–121. https://doi.org/10.1038/nature23271<br>
 +
 
 +
7. Pardee, K., Green, A. A., Takahashi, M. K., Braff, D., Lambert, G., Lee, J. W., Ferrante, T., Ma, D., Donghia, N., Fan, M., Daringer, N. M., Bosch, I., Dudley, D. M., O'Connor, D. H., Gehrke, L., & Collins, J. J. (2016). Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components. Cell, 165(5), 1255–1266. https://doi.org/10.1016/j.cell.2016.04.059<br>
 +
 
 +
8. Chappell, J., Westbrook, A., Verosloff, M., & Lucks, J. B. (2017). Computational design of small transcription activating RNAs for versatile and dynamic gene regulation. Nature communications, 8(1), 1051. https://doi.org/10.1038/s41467-017-01082-6<br>
 +
 
 +
9. Sadat Mousavi, P., Smith, S. J., Chen, J. B., Karlikow, M., Tinafar, A., Robinson, C., Liu, W., Ma, D., Green, A. A., Kelley, S. O., & Pardee, K. (2020). A multiplexed, electrochemical interface for gene-circuit-based sensors. Nature chemistry, 12(1), 48–55. https://doi.org/10.1038/s41557-019-0366-y<br>
 +
 
 +
10. Hong, F., Ma, D., Wu, K., Mina, L. A., Luiten, R. C., Liu, Y., Yan, H., & Green, A. A. (2020). Precise and Programmable Detection of Mutations Using Ultraspecific Riboregulators. Cell, 180(5), 1018–1032.e16. https://doi.org/10.1016/j.cell.2020.02.011<br>
 +
 
 +
11. Pardee K, Green AA, Takahashi MK, et al. Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components. Cell 2016; 165(5): 1255-66. <br>
  
References:
 
Green, A. A., Silver, P. A., Collins, J. J., & Yin, P. (2014). Toehold switches: de-novo-designed regulators of gene expression. Cell, 159(4), 925-939.
 
Pardee, K., Green, A. A., Takahashi, M. K., Braff, D., Lambert, G., Lee, J. W., ... & Daringer, N. M. (2016). Rapid, low-cost detection of Zika virus using programmable biomolecular components. Cell, 165(5), 1255-1266.
 
  
 
<!-- Add more about the biology of this part here
 
<!-- Add more about the biology of this part here

Latest revision as of 01:30, 28 October 2020


zr31 Toehold Switch for miR-31 Detection

Description

zr31 toehold switch is a regulatory part for the downstream gene. With this part, the protein expression can be controlled by the miR-31, which is a biomarker for oral squamous cell carcinoma (OSCC)[1][2][3][4]. The sequence of the toehold switch can be divided into 5 regions: a trigger binding site (TBS), a stem region, a loop region containing ribosome binding site (RBS), another complimentary stem region with a start codon, and a linker. When the miR-31 binds with the TBS, the hairpin structure of the toehold can be opened up and the ribosomes can bind with the RBS, triggering the translation of the downstream reporter. zr31 Toehold Switch can be combined with different kinds of reporters, and further applied to oral cancer detection.

Design & Model

The design of the toehold switch was mainly based on previous research[5][6][7][8][9][10]. For the zr31 toehold switch, we adopted the loop structure from Green et al., 2016[11], and a random linker structure we designed. Using NUPACK analysis and Vienna binding models, we designed the sequence of the toehold switch. (See our model page: https://2020.igem.org/Team:CSMU_Taiwan/Model )


Figure 1. NUPACK analysis result
Figure. 2. ViennaRNA Package result

















NUPACK analysis suggested the MFE (minimum free energy) RNA structure at 37℃, whose free energy was -22.00 kcal/mol. As for the Vienna binding, the black line in the figure indicates the amount of energy required to open the secondary structures of the TBS. The line red indicates the amount of energy required to open the secondary structure after the binding of the trigger. As a result, this indicates that zr31 will successfully be bound and open the locked structure.

Characterization using invertase

The 2020 iGEM CSMU-Taiwan characterized the zr31 toehold switch with T7 promoter (BBa_I719005), invertase reporter protein (BBa_K3431000), and T7 terminator(BBa_K731721). We built up a composite part BBa_K3431023 to test its functionality. The plasmids were transcribed and translated with the PURExpress® In Vitro Protein Synthesis Kit (New England Biolabs) at 37℃ for 2 hours. We would then add 5μl of 0.5M sucrose, and measured the glucose concentration with Bionime Rightest™ GM550 glucose meter after 30 minutes of enzymatic reaction time. In our experiments, the ON state refers to the conditions with miRNA triggers; while the OFF state means that there was no miRNA in the environment. We calculated the ON/OFF ratio of the toehold switch, which is defined as “the glucose concentration of the ON state/ the glucose concentration of the OFF state”.


Fig. 3. The glucose productions of the zr31 toehold switch-regulated invertase in different states. The blue bar refers to the OFF state (not added with miRNA). The green bar refers to the ON state (added with miR-31 trigger). The yellow bar refers to the state with non-related RNAs (added with miR-191). The pink bar refers to the state with non-related RNAs (added with miR-223).
Results
The glucose concentration in the ON state with miR-31 is 310.67 mg/dL, indicating the high sensitivity of the toehold switch. The ON/OFF ratio with miR-31 is 2.65, which suggested the regulatory function of the toehold switch. By contrast, in the experiment of negative selection, the ON/OFF ratios with miR-191 and miR-223 are 1.46 and 1.21, respectively. These ratios are close to 1, meaning the zr31 toehold switch has high specificity. As a result, the zr31 toehold switch has been proven to be useful for miR-31 detection.

Characterization using invertase

To understand the correlation of the trigger amount and the glucose production, we added different amounts of miR-31 to the protein synthesis kit and produced the proteins at 37℃ for 2 hours. We would then add 5μl of 0.5M sucrose and measured the glucose concentration with the glucose meter after 30 minutes.

Fig. 4. Glucose production under different amounts of miR-31.
Results
As shown above, the glucose concentration rose as the miR-31 triggers increased, representing a positive correlation.

References

1. Liu, C.-J., Lin, S.-C., Yang, C.-C., Cheng, H.-W., & Chang, K.-W. (2011). Exploiting salivary miR-31 as a clinical biomarker of oral squamous cell carcinoma. Head & Neck, 34(2), 219–224. https://doi.org/10.1002/hed.21713

2. Mazumder, S., Datta, S., Ray, J. G., Chaudhuri, K., & Chatterjee, R. (2019). Liquid biopsy: miRNA as a potential biomarker in oral cancer. Cancer epidemiology, 58, 137–145. https://doi.org/10.1016/j.canep.2018.12.008

3. Min, A., Zhu, C., Peng, S., Rajthala, S., Costea, D. E., & Sapkota, D. (2015). MicroRNAs as Important Players and Biomarkers in Oral Carcinogenesis. BioMed Research International, 2015, 1–10. https://doi.org/10.1155/2015/186904

4. Momen-Heravi, F., Trachtenberg, A. J., Kuo, W. P., & Cheng, Y. S. (2014). Genomewide Study of Salivary MicroRNAs for Detection of Oral Cancer. Journal of Dental Research, 93(7_suppl), 86S-93S. https://doi.org/10.1177/0022034514531018

5. Green, A. A., Silver, P. A., Collins, J. J., & Yin, P. (2014). Toehold switches: de-novo-designed regulators of gene expression. Cell, 159(4), 925–939. https://doi.org/10.1016/j.cell.2014.10.002

6. Green, A. A., Kim, J., Ma, D., Silver, P. A., Collins, J. J., & Yin, P. (2017). Complex cellular logic computation using ribocomputing devices. Nature, 548(7665), 117–121. https://doi.org/10.1038/nature23271

7. Pardee, K., Green, A. A., Takahashi, M. K., Braff, D., Lambert, G., Lee, J. W., Ferrante, T., Ma, D., Donghia, N., Fan, M., Daringer, N. M., Bosch, I., Dudley, D. M., O'Connor, D. H., Gehrke, L., & Collins, J. J. (2016). Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components. Cell, 165(5), 1255–1266. https://doi.org/10.1016/j.cell.2016.04.059

8. Chappell, J., Westbrook, A., Verosloff, M., & Lucks, J. B. (2017). Computational design of small transcription activating RNAs for versatile and dynamic gene regulation. Nature communications, 8(1), 1051. https://doi.org/10.1038/s41467-017-01082-6

9. Sadat Mousavi, P., Smith, S. J., Chen, J. B., Karlikow, M., Tinafar, A., Robinson, C., Liu, W., Ma, D., Green, A. A., Kelley, S. O., & Pardee, K. (2020). A multiplexed, electrochemical interface for gene-circuit-based sensors. Nature chemistry, 12(1), 48–55. https://doi.org/10.1038/s41557-019-0366-y

10. Hong, F., Ma, D., Wu, K., Mina, L. A., Luiten, R. C., Liu, Y., Yan, H., & Green, A. A. (2020). Precise and Programmable Detection of Mutations Using Ultraspecific Riboregulators. Cell, 180(5), 1018–1032.e16. https://doi.org/10.1016/j.cell.2020.02.011

11. Pardee K, Green AA, Takahashi MK, et al. Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components. Cell 2016; 165(5): 1255-66.


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]