Difference between revisions of "Part:BBa K3431007"

(→‎Characterization using invertase)
(→‎References)
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===References===
 
===References===
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1. 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>
+
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. 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>
+
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. 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>
+
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. 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>
+
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. 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>
+
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. 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>
+
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 AA, Takahashi MK, et al. Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components. Cell 2016; 165(5): 1255-66. <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>
  
  

Revision as of 12:21, 27 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]