BLUEPRINT

OVERVIEW

“APTASTELES” is a novel aptamer-based diagnostic kit for Polycystic Ovarian Syndrome (PCOS). Based on an intensive literature review, we selected ten biomarkers present in the blood, including proteins, hormones and miRNAs. We used short-chain oligonucleotides called aptamers which are specific to their targets. Our kit would detect an array of biomarkers with the help of light-up RNA aptamers, which exhibit fluorescence when bound to a fluorophore. This fluorescence is detected using a PIN photodiode which gives a voltammetric output. 

Two separate concentration-dependent detection systems are devised for hormones/proteins and miRNAs.

In this section, we will be going through these project modules:-

  1. Biomarkers
  2. Protein and Hormone Detection
  3. miRNA Detection
  4. Kit 

BIOMARKERS

A biomarker is defined as a characteristic that can be measured to indicate dysfunction of normal biological processes, pathogenic processes or responses to an exposure or intervention [1]. The selection of biomarkers is a crucial step in any diagnostic project. Several parameters are responsible for the selection process.






After reviewing the literature and databases, we found a lot of biomarkers which are associated with PCOS. However, very little research has been done on most of these biomarkers, which include various biomolecules, proteins, hormones and RNAs. With limited data available, we chose a combination of biomarkers based on their sensitivity, specificity and ease of detection. 


Our biomarkers of interest are:

Prolidase

Prolidase is an enzyme that catalyses the rate-limiting step during collagen recycling and is essential in protein metabolism, collagen turnover, and matrix remodelling [2]. 

It has been found that prolidase levels increase with the increase in the number of cysts in the ovary. The sensitivity and specificity of prolidase levels (with a cut-off value of 621.54 U/l) were predicted to be 93.76% and 89.34%, respectively, from literature [3].

Irisin

Irisin is a protein that plays a vital role in energy metabolism in each organ in the body and in regulating metabolic diseases such as obesity and diabetes.

Circulating irisin levels are associated with hyperandrogenism, one of the symptoms associated with PCOS [4].

C-Reactive Protein (CRP)




CRP is a pentameric protein in blood serum, released in excess in response to the production of IL-6 by macrophages. Evidence has been found that links enhanced levels of CRP to PCOS [5].



Testosterone

Testosterone is the sex hormone responsible for the growth, repair, and maintenance of reproductive tissues.

It has been found that testosterone levels (both total and free testosterone) are significantly elevated in PCOS patients and can be a useful biomarker to indicate PCOS [6].



Luteinizing Hormone (LH)




Luteinizing Hormone (LH) is a gonadotropin responsible for the evolution and maturation of the corpus luteum in healthy females.

Studies have shown that people with PCOS exhibit significantly higher levels of LH due to higher adrenal androgen activity [6].



Follicle-stimulating hormone (FSH)




Follicle-stimulating hormone (FSH) is another gonadotropin that controls ovarian follicular maturation. It also plays a vital role in sexual development.

Like LH, FSH also increases in concentration in the blood due to high levels of androgens in people with PCOS [6].



miRNAs


miRNAs are short non-coding RNAs of approximately 22 nucleotides that regulate gene expression at the post-translational level. Recent studies have shown that altered miRNA expression is essential in several cancers and tumour proliferation.

Several studies have found that high expression of serum miRNAs has a direct correlation with PCOS. A particular study showed that a combination of the miRNAs used for diagnosis improved the specificity and sensitivity. After scouring the literature and analysing the expression levels of miRNAs, we chose a combination of four miRNAs associated with PCOS; miR-222, miR-27a-5p, miR-30c, and miR-146a [7,8].

PROTEIN AND HORMONE DETECTION

Protein and hormonal biomarkers were detected using a dual aptamer technique as described below. We incorporated signal amplification to account for the low concentration of biomarkers in plasma. This is based on the in-vitro transcription of the cDNA template into multiple copies of broccoli RNA light-up aptamer. A similar technique has been used to detect the food-borne pathogen Staphylococcus aureus in food and water samples [9, 10].

While designing the aptamers for this technique, we realised that there are no existing well-characterised aptamers for irisin and prolidase. To solve this issue, we planned to perform Systematic Evolution of Ligands by Exponential Enrichment (SELEX), a method used to raise aptamers against target molecules (For more information, visit background). We chose existing aptamers from the literature for the rest of the biomarkers.

We went by two approaches for designing the aptamer sequences. The first approach requires an extension polymerase (phi29 DNA polymerase) and the second approach works independently of the extension polymerase. 

Both approaches comprise two modules:

  1. Recognition 
  2. Amplification

RECOGNITION MODULE

It includes a biomarker-specific DNA aptamer and a hybridised trigger sequence (the template strand for transcription). 

AMPLIFICATION MODULE

It consists of a target (located at the 5' end), ssDNA sense T7 promoter (in the middle) and ssDNA sense broccoli light-up aptamer (located at the 3' end) strands. 

MECHANISM

Strand displacement

In the presence of a biomarker, the trigger strand is displaced and binds to the complementary target sequence of the amplification module. 

Extension

The sequential addition of phi29 DNA polymerase leads to the extension and formation of 3' to 5' template strands for broccoli RNA aptamers. 

In-vitro transcription and visualisation 

In-vitro transcription of this dsDNA using T7 RNA polymerase forms large amounts of broccoli RNA aptamers. Upon proper folding of accumulated RNA aptamers, the G-quadruplex binding site for fluorogenic 3,5-DiFluoro-4-HydroxyBenzylidene Imadazolinone (DFHBI) is created (Click here for more information on light-up aptamers). Free DFHBI or DFHBI-1T dye alone produces low or no fluorescence. Broccoli-DFHBI pairs (excitation and emission maxima at 447 nm and 501 nm, respectively) give a high fluorescence read-out proportional to the amount of broccoli produced and, therefore, corresponds to the concentrations of the biomarker [11].


DESIGN



For further information regarding the design of the amplification and recognition module, refer to the Model page.


(Comment after the competition) NOTE: 

  • After troubleshooting our results, we realised that “Approach 1” wouldn’t be feasible due to the incorrect polarity of the sequences. In theory, this approach is not possible.
  • We realised that “Approach 2” is the only feasible solution and this approach could be modified to detect proteins and hormones involving phi 29 DNA polymerase. 
  • The suggested modifications in “Approach 2” are:
  1. Design the trigger sequence (removing sense T7 promoter sequence at 3’ end) complementary to an arbitrary sequence of the biomarker-specific aptamer.
  2. The trigger sequence can have a few bases complementary to the antisense T7 promoter sequence at the 3’ end. Sequentially added phi 29 polymerases (after strand displacement) will form a double-stranded DNA promoter to initiate the transcription by T7 RNA polymerase.
  3. Parameters like free energy of binding and secondary structures of all possible trigger-target sequences and trigger-aptamers sequences should be verified. This is necessary in order to maintain the specificity of binding in the presence and absence of the protein/hormone/ligand.

miRNA DETECTION

The miRNA concentration in the blood is significantly low to be detected with the current methods available. Therefore, we are incorporated the miRPA technique inspired by the iGEM 2021 team City of London, UK. They modified the isothermal amplification technique Recombinase Polymerase Amplification (RPA) to amplify miRNA using a reverse transcription-like strategy [12,13].

miRPA 

The components of the system for the working of miRPA are:

  1. Two probes ( a 5' probe with a free hydroxyl at the 3' end and a 3' probe with a phosphate group on the 5' end. These two probes have sequences complementary to the miRNA and overhang sequences)
  2. Ligase (that help in the bonding of the phosphate group and the hydroxyl group)
  3. Strand-displacing polymerase (that helps in the isothermal amplification)
  4. Forward primer complementary to the 3' probe and reverse primer.
  5. Recombinase (which helps in finding the homology in the template strand)
  6. Single-strand binding proteins (that stabilise the single-stranded sequences).

 The reaction happens as follows:




The amplified miDNA is detected by FASTmiR.

FASTmiR 





FASTmiR stands for Fluorescence Aptamer Sensor For Tracking miRNAs [14]. It is an RNA-based sensor for in-vitro quantification of miRNAs. One FASTmiR sensor is made out of three modules: 

  1. A small RNA sensing region (complementary sequence of our desired miRNA)
  2. The base of a three-way junction (3WJ) and 
  3. A modified form of spinach light-up aptamer


The sensor goes through three different states upon miRNA binding:

  1. In the native state or when there's no miRNA present, it's called an OFF state.

  2. When miRNA binds, and the structure of the miRNA starts changing, then it's called TRANSITION. 

  3. Finally, when the miRNA binds correctly, forming a 3WJ, it's called the ON state. 


In the ON state, spinach forms a unique structure called G-quadruplex. In that region, fluorescent dye DFHBI binds and shows fluorescence. 


MECHANISM

The FASTmiR sensor in its default state would be in a closed conformation where no binding pocket would be available for the dye, DFHBI, to bind. However, once the specific miRNA for the sensor comes into contact, the complementarity between the RNA sensing region and the RNA would facilitate the change in the conformation of the aptamer. It undergoes a transition, where it finally reaches a structure containing a three-way junction and a G-quadruplex. The presence of G-quadruplex helps in the binding of DFHBI to a site in the sensor, hence giving out fluorescence.



DESIGN



For further information regarding the design of our FASTmiR sensor, refer to the Model Page

KIT


DESIGN

This project's ultimate goal is to design and build an easy-to-use, point-of-care device for diagnosing PCOS. Our kit contains a microfluidic chip and an optical detection system. The microfluidic chip will have ten separate channels containing aptamers, buffers, dye and other reagents in a cell-free system for each type of biomarker. The optical detection device will consist of a Photodiode, bandpass filter and circuit board. Refer to Hardware to learn more about it.



DETECTION

For both detection systems, we see a higher level of fluorescence output achieved for a higher level of biomarker concentration at a particular time. The concept of distinguishing normal and abnormal levels of biomarkers is based on setting threshold values for fluorescence outputs at a specific time. The information reagrding the reaction kinetics is available in Model.

To do this, experimental data is required. Hence an experimental calibration curve was plotted and fitted after the required experimental procedures. Details of the data is explained in Results.



Figure1: Calibration curve for broccoli light-up aptamer



From the models, we see a significant difference in the concentration of broccoli aptamer in normal and PCOS cases at a particular time. We know that greater the concentration of light-up aptamers, greater will be the fluorescence. Using the calibration curve, we plotted a theoretical fluorescence vs. time graph.



Figure2: a)Model prediction of the rate of formation of Broccoli for different concentrations of biomarkers.
b) Theoretical graph for fluorescence vs time-based on Figure1. and Figure2a. data



Using these techniques, thresholds for fluorescence can be set to discriminate between normal and abnormal concentrations of our biomarkers. To decide on what threshold fluorescence should be set, a study needs to be conducted based on our system, and a model needs to be developed to predict the best cut-off threshold. Hence, once a threshold has been established, we can predict the concentration of biomarkers based on the fluorescence readouts. 

Suppose that the fluorescence is greater than the threshold at a given time. In that case, we can say that the biomarker concentration is higher than normal, and that person might be prone to several disorders and diseases related to that biomarker.

In the kit, however, fluorescence would be detected by a photodiode, giving a voltage output. After experimenting with our photodiode, using another calibration plot between voltage and concentration of broccoli aptamers, we can set the voltammetric threshold to detect our biomarker concentrations based on the photodiode output.

REFERENCES

  1. FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource [Internet]. Silver Spring (MD): Food and Drug Administration (US); 2016-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK326791/ Co-published by National Institutes of Health (US), Bethesda (MD).
  2. Eni-Aganga, I., Lanaghan, Z. M., Balasubramaniam, M., Dash, C., & Pandhare, J. (2021). PROLIDASE: A Review from Discovery to its Role in Health and Disease. Frontiers in molecular biosciences, 8, 723003.
  3. Bhatnager, R., Nanda, S., & Dang, A. S. (2018). Plasma prolidase levels as a biomarker for polycystic ovary syndrome. Biomarkers in Medicine, 12(6), 597-606.
  4. Zhang, L., Fang, X., Li, L., Liu, R., Zhang, C., Liu, H., Tan, M., & Yang, G. (2018). The association between circulating irisin levels and different phenotypes of polycystic ovary syndrome. Journal of endocrinological investigation, 41(12), 1401–1407. 
  5. Sumithra, N. U., Lakshmi, R. L., Leela Menon, N., Subhakumari, K. N., & Sheejamol, V. S. (2015). Evaluation of Oxidative Stress and hsCRP in Polycystic Ovarian Syndrome in a Tertiary Care Hospital. Indian journal of clinical biochemistry : IJCB, 30(2), 161–166. 
  6. Abdelazim, I. A., Alanwar, A., AbuFaza, M., Amer, O. O., Bekmukhambetov, Y., Zhurabekova, G., Shikanova, S., & Karimova, B. (2020). Elevated and diagnostic androgens of polycystic ovary syndrome. Przeglad menopauzalny = Menopause review, 19(1), 1–5. 
  7. Sørensen, A. E., Wissing, M. L., Salö, S., Englund, A. L. M., & Dalgaard, L. T. (2014). MicroRNAs related to polycystic ovary syndrome (PCOS). Genes, 5(3), 684-708.
  8. Long, W., Zhao, C., Ji, C., Ding, H., Cui, Y., Guo, X., ... & Liu, J. (2014). Characterization of serum microRNAs profile of PCOS and identification of novel non-invasive biomarkers. Cellular Physiology and Biochemistry, 33(5), 1304-1315.
  9. Sheng, L., Lu, Y., Deng, S., Liao, X., Zhang, K., Ding, T., ... & Li, J. (2019). A transcription aptasensor: amplified, label-free and culture-independent detection of foodborne pathogens via light-up RNA aptamers. Chemical Communications, 55(68), 10096-10099.
  10. Kwoh, D. Y., Davis, G. R., Whitfield, K. M., Chappelle, H. L., DiMichele, L. J., & Gingeras, T. R. (1989). Transcription-based amplification system and detection of amplified human immunodeficiency virus type 1 with a bead-based sandwich hybridization format. Proceedings of the National Academy of Sciences, 86(4), 1173-1177.
  11. Filonov, G. S., & Jaffrey, S. R. (2016). RNA imaging with dimeric broccoli in live bacterial and mammalian cells. Current protocols in chemical biology, 8(1), 1-28.
  12. Team:City of London UK - 2021.igem.org
  13. Wee, E. J., & Trau, M. (2016). Simple isothermal strategy for multiplexed, rapid, sensitive, and accurate miRNA detection. Acs Sensors, 1(6), 670-675.
  14. Huang, K., Doyle, F., Wurz, Z. E., Tenenbaum, S. A., Hammond, R. K., Caplan, J. L., & Meyers, B. C. (2017). FASTmiR: an RNA-based sensor for in vitro quantification and live-cell localization of small RNAs. Nucleic acids research, 45(14), e130-e130.
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