Team:CSMU Taiwan/Proof Of Concept

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Proof of Concept


In our project, the detection of OSCC (Oral Squamous Cell Carcinoma) was composed of three aspects: miRNAs, toehold switches, and the relationship between miRNAs and the output display. To validate these concepts, we underwent paper studies to verify the accuracy of the biomarkers we selected, which was the miRNAs. We ran the thermodynamic analyses to calculate the free energy of toehold switches in order to represent the stability for miRNA binding and suitability for our experiments after deciding to use miRNA as the trigger. Finally, as invertase converts sucrose into fructose and glucose, we measured the glucose concentration to establish the dose-dependency of miRNA expression so as to figure out whether invertase and glucose expression are positively correlated.

Finding Our Biomarkers - miRNAs

Among several biomarkers, we specifically chose salivary miRNAs as they exist in oral cavity and can directly contact carcinomatous cells that reflect the microscopic alterations for stages of cancer.1 Furthermore, salivary miRNAs are often encapsulated by lipoprotein and conjugated with ribonucleoprotein, making miRNAs highly stable in salivary environments.2

In the case of differentiating OPMD (Oral Potentially Malignant Disorder) patients, miRNA 21 possesses the ability to distinguish early-stage OPMD, while miRNA 31 is capable of distinguishing late-stage malignant OPMD from healthy patients. In particular, miRNA 31 possesses the best performance on expression level differences with 9.84 fold upregulation compared to healthy groups. This unique characteristic of miRNA 31 allows the earlier discovery of malignant OPMD before progressing into OSCC. Furthermore, according to the work of Min et al., miRNA 146 is highly sensitive and specific to the detection of OSCC. Hence, miRNA 21, 31, 146 are the major targets in our detection scheme for OPMD and OCSS. We present the related characteristics for the three miRNA biomarkers in Table 1.

Fold of Change Characteristics Reference
miRNA 21 5.20 upregulation Detect early-stage of OPMD and OSCC Zahran, F., Ghalwash, D., Shaker, O., Al-Johani, K., & Scully, C. (2015). Salivary microRNAs in oral cancer. Oral diseases, 21(6), 739–747.
miRNA 31 9.84 upregulation Detect Malignant OPMD and OSCC Liu, C. J., Lin, S. C., Yang, C. C., Cheng, H. W., & Chang, K. W. (2012). Exploiting salivary miR-31 as a clinical biomarker of oral squamous cell carcinoma. Head & neck, 34(2), 219–224.
miRNA 146 4.02 upregulation Detect OSCC with High Sensitivity and Specificity Min, S. K., Jung, S. Y., Kang, H. K., Park, S. A., Lee, J. H., Kim, M. J., & Min, B. M. (2017). Functional diversity of miR-146a-5p and TRAF6 in normal and oral cancer cells. International journal of oncology, 51(5), 1541–1552.
Table 1. A comparison between the selected miRNA biomarkers

In addition, a study by Wang et al.3 indicated that toehold switch is a suitable miRNA detection system, so we decided to adapt toehold switch to the next part of our project.

Validating our Ribosensing Device - Toehold Switches

In order to detect the presence and amounts of our selected miRNAs for monitoring the progression of OSCC, we incorporated a riboregulatory sensing device - Toehold Switches - to convert the signal level of miRNA into the downstream expression of the reporter gene: invertase.

As demonstrated by previous researches, toehold switches express exceptional specificity and sensitivity on detecting targeted RNA sequence 4, 5. In other words, toehold switches can recognize targeted RNA sequences with minimal cross-reactivity, and the presence of the targeted RNA sequence can trigger a significant and proportional expression level of downstream reporter proteins 6. Also, toehold switches are optimizable depending on the desired targeted RNA sequences. Coupling the modularity of toehold switch with further integration of an in vitro protein synthesis system, toehold switches are suitable for medical detection of RNA biomarkers under a field-deployable POC (point of care) device 7. This combination of detecting accuracy and flexibility is the reason why we adapted the toehold switch sensing mechanism to detect our selected miRNAs and convert their signal levels for later application on a glucose meter.

Before we conducted our experiment, we made sure the modelling results led to a functional toehold switch. This was confirmed by the Vienna RNA package that predicts the interaction between our miRNAs and toehold switches. The result indicated that the toehold wouldn’t unwind spontaneously without miRNA presence, as the black line shows in Figure 1. (see Model for more information). Based on our dry lab data, we proved that the adaptation of toehold switch is rational with miRNA.

We modified the in vitro protein synthesis kit to figure out the best reaction environment by proving that the differences between the on and off stages are significant . The on and off stages represent the production of protein with the presence or absence of trigger respectively (see Measurement) . We successfully found out the optimal condition for toehold switches based on the ON/OFF ratio graph. (Figure 2)

Figure 1. Vienna_RNAup Analysis on a toehold switch (zr31) with its targeted miRNA

Figure 2. ON/OFF Ratios under Different Conditions of in vitro protein synthesis kit

As for the wet lab experiments, we put all twenty one toehold switches into the modified conditions. The majority of on/off ratio of toehold switches are above one, which represents that the toeholds can be successfully controlled by miRNAs. An ON/OFF ratio below one means the measuring error, an ON/OFF ratio equal to one means no regulation, and an ON/OFF ratio higher than one represents successful regulation. This result indicates that targeted miRNAs can effectively trigger the expression level of our toehold switches, validating the regulatory function of toehold switch mechanisms.

Figure 3. ON/OFF Ratios for toehold switches tailored for miRNA 31 and 146 under different trigger RNAs

Demonstrating the Reliability of Output Scheme - Dose-Dependency

In order to measure the quantitative value of the targeted miRNAs, we conducted a test with two toehold switches, zr31 and zr146_A, to examine the dose-dependency between the amounts of targeted miRNA and the resulting glucose concentrations. The glucose is converted by invertase from sucrose. The invertase activity is generally measured by glucose expression per minute. This allows us to directly link the quantity of miRNA to the glucose produced.8 As shown in Figure 4, when the amounts of targeted miRNAs increase, the glucose concentration also rises in a positive association.

Figure 4. Regression analyses of the amounts of miRNAs (31, 146) with glucose concentrations


With the backup from previous researches and our results in modeling and experiments, we validated the feasibility of our toehold switch sensing device on detecting OSCC, and proposed to conduct further experiments on verifying the relationship between our toehold switches and enzymatic activity of invertase.

Firstly, we verified through paper studies that our selected miRNA biomarkers - miRNA 21, 31, and 146 - are capable of distinguishing the progression of OSCC and our toehold switch sensing mechanism is functional on miRNA. Next, we modeled the binding energy between toehold switches and targeted miRNA and postulated a workable toehold switch sequence. Our experimental results supported the practicality of toehold switches with ON/OFF Ratios that are larger than one. This represents that our toehold switches can regulate the expression of invertase along with the presence of targeted miRNA. Finally we established a significant positive correlation between the amounts of miRNA and the output glucose concentration.

With the combination of the diagnostic ability of selected miRNAs, the regulatory function of toehold switches, and the dose-dependency of our detection system, we proposed our solution - miRNA.DOC - to implement these concepts for detection of OSCC. Additionally, we hope to further establish a validated mathematical function to correctly infer the amounts of miRNA from output glucose concentration.


  1. Cristaldi, M., Mauceri, R., Di Fede, O., Giuliana, G., Campisi, G., & Panzarella, V. (2019). Salivary biomarkers for Oral Squamous Cell Carcinoma diagnosis and follow-up: current status and perspectives. Frontiers in Physiology, 10. Mazumder S, Datta S, Ray JG, Chaudhuri K, Chatterjee R. Liquid biopsy: miRNA as a potential biomarker in oral cancer. Cancer Epidemiol. 2019 Feb;58:137-145. doi: 10.1016/j.canep.2018.12.008. Epub 2018 Dec 19. PMID: 30579238.
  2. Rapado-González, Ó., López-López, R., López-Cedrún, J. L., Triana-Martínez, G., Muinelo-Romay, L., & Suárez-Cunqueiro, M. M. (2019). Cell-free microRNAs as potential oral cancer biomarkers: from diagnosis to therapy. Cells, 8(12), 1653.
  3. Wang, S., Emery, N. J., & Liu, A. P. (2019). A novel synthetic toehold switch for microRNA detection in mammalian cells. ACS synthetic biology, 8(5), 1079-1088.
  4. 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.
  5. Tang Y , Li H , Li B . Homogeneous and universal transduction of various nucleic acids to an off-shelf device based on programmable toehold switch sensing. Chem Commun (Camb). 2020 Feb 25;56(16):2483-2486. doi: 10.1039/c9cc09154h. PMID: 32002523.
  6. Takahashi MK, Tan X, Dy AJ, Braff D, Akana RT, Furuta Y, Donghia N, Ananthakrishnan A, Collins JJ. A low-cost paper-based synthetic biology platform for analyzing gut microbiota and host biomarkers. Nat Commun. 2018 Aug 21;9(1):3347. doi: 10.1038/s41467-018-05864-4. PMID: 30131493; PMCID: PMC6104080.
  7. Geraldi, A., & Giri-Rachman, E. A. (2018). Synthetic biology-based portable in vitro diagnostic platforms. Alexandria journal of medicine, 54(4), 423-428.
  8. Boguslawski G. A simplified method for measuring activity of beta-D-fructofuranoside fructohydrolase (invertase). J Appl Biochem. 1983 Feb-Apr;5(1-2):132-5. PMID: 6678934.