UR igem wiki

Wet Lab Design

  • Identifying Biomarkers
  • There is a startling lack of research surrounding the pathophysiology of endometriosis, which causes great difficulty in identifying which exact biomarkers are the most reliable indicators of this chronic disease. While researchers have discovered the aberrant tissue growth and hormone dysregulation that accompany the disease, current literature seems to disagree on the specific molecular basis of this condition (Malvezzi et al., 2019). Some experts in the field have pointed to altered immunity as the main cause while others focused on errors in the decidualization pathway, hormone dysregulation, or cancer-like mechanisms in the endometrial cells (Karimi-Zarchi et al., 2016, Sabbaj et al., 2011, Warren et al., 2018). At first, this made it difficult to design a comprehensive diagnostic for endometriosis. However, we realized that all of these approaches to endometriosis revealed a small part of the puzzle and our team was eager to put the pieces together.

    We identified the symptoms and pathologies associated with each biomarker in order to create a comprehensive diagnostic panel that included molecules involved in each theory of endometriosis. The first three biomarkers we selected, IGFBP-1, CA125, and IL-6, were chosen as a result of our sensitivity and specificity modeling, which indicated that these biomarkers had great potential for predicting endometriosis. The second three biomarkers we selected, IL-1β, TNF-𝛼, and IL-8, were chosen since we believed that these biomarkers had great therapeutic potential for the treatment of inflammatory symptoms (monoclonal antibodies for these biomarkers have been FDA-approved for various conditions). As such, we believe this panel serves as both a diagnostic tool and provides the opportunity for personalized medicine for the treatment of endometriosis symptoms.

    Altered Uterine Environment

    Insulin growth factor binding protein-1 (IGFBP-1) binds to insulin growth factor (IGF-1) in the cell to regulate cell and tissue growth (Frystyk et al., 1998). The failure of endometrial cells to undergo decidualization in the uterus, an important step in the menstrual cycle as the womb prepares for implantation, has been associated with aberrant endometrial-like tissue growth outside of the uterine cavity (Okada et al., 2018). Recent studies have linked a reduction in decidualization capacity in the endometrial stromal cells of endometriosis patients, as measured by levels of IGFBP-1 (Nayyar et al., 2020, Warren et al., 2018). This indicates the IGFBP-1 may be an important indicator of the identifying changes that occur in the uterine lining for the pathophysiology of endometriosis.

    Inflammation and Irritation

    Cytokines and interleukins have been a persistent area of interest to endometriosis researchers. In particular, elevated levels of interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor (TNF-𝛼) have all been heavily implicated in the pathophysiology of this chronic disease (Malutan et al., 2015). While these molecules were originally associated with infertility, more recent research has shown that these molecules are more important in the pain and inflammation underlying the progression of this disease (Malvezzi et al., 2019).

    Aberrant Tissue Growth

    CA125 first became a growing area of research when it was discovered as a biomarker for ovarian cancer in the 1980s (Perez & Gipson, 2008). Since then, it has also been shown to be elevated in the blood of endometriosis patients (Karimi-Zarchi et al., 2016). Although it was implicated in other diseases, our modeling team demonstrated that CA125 led to a significant increase in the accuracy of our diagnostic panel and so it was included in our design.

    Biomarker Thresholds

    Threshold values, sensitivities, and specificities for the diagnosis of endometriosis were identified through previous literature (Bedaiwy, 2002; Galo et al., 2005; Hirsch et al., 2016; Juul et al., 1997; Malutan et al., 2015; Ohata et al., 2008; Warren et al., 2018). IGFBP-1, CA125, and IL-6 were included as part of the panel due to the results of our sensitivity and specificity modeling while IL-1β, TNF-𝛼, and IL-8 were included due to their therapeutic potential for the treatment of inflammatory symptoms.

  • Creating a Point of Care Diagnostic
  • Our next challenge was determining the best point of care diagnostic for our biomarkers. We wanted a diagnostic tool that would provide sensitive, reliable results in a clinical or laboratory setting. As such, we decided on a lateral flow assay (LFA) enhanced with gold nanoparticle (GNP) detection.

    How a Lateral Flow Assay Works

    A lateral flow assay (LFA) detects the presence of target molecules in a sample using a sandwich-style immunoassay approach. At the sample pad, where the sample is applied, there is a fixed concentration of labeled antibodies on the membrane. As the sample soaks into the pad and begins flowing down the test strip, the labeled antibodies bind to their target molecules. Once the sample reaches the test line where the target molecule is bound to by a second, immobilized antibody while free labeled antibodies can continue to travel down the strip to a control line (Sajid et al., 2015). Normally, these labeled antibodies are labeled using dye. However, our team found that conjugating these antibodies to gold nanoparticles (GNP) significantly increased the sensitivity of our design to the range of our desired concentrations with a detection threshold of less than 0.1 ng/mL (Teerinen et al., 2014).

  • Using Synthetic Biology
  • With the socioeconomic gradient in health resulting in an even greater misdiagnosis rate of endometriosis, we wanted to make our design as accessible as possible (Bougie et al., 2019). While lateral flow assays are a great option for a point of care diagnosis, without mass production, they can be expensive to construct due to the use of multiple antibodies per test strip. The high cost of antibodies is typically due to the laborious production process, involving the inoculation of laboratory animals with the desired biomarker and then harvesting cells for collection of the resulting antibody (The Humane Society of the United States, 2005). In addition to the ethical concerns of this costly method, this could present an issue to laboratories and clinics with limited budgets for diagnostic tests, further exacerbating the under diagnosis of this disease in underserved areas. To address this concern, we decided to use synthetic biology in a way that could globally increase the accessibility of our design and other applications of immunoassays.
    Recent research has shown that it is possible to produce full length antibodies in a specific strain of E. coli (Robinson et al., 2015). This strain, SHuffle, has an engineered oxidative cytoplasm that allows for the proper folding and formation of disulfide bonds in the antibody structure. Furthermore, it has additional chaperones to help mediate this process (Lobstein 2012 et al., 2012). As such, this chassis organism was perfect for allowing us to develop “plug and play” plasmids in which you could simply insert your variable regions of choice into our designs and produce your target antibody. This helps lower the cost of creating and marketing immunoassays and avoids the necessity of harvesting antibodies from animals.

  • Estrogen Response Element Circuit
  • Estrogen and Endometriosis

    Endometriosis has been defined as an estrogen-dependent disease, in which increased production of the steroid hormone 17β-estradiol (E2) has been implicated in endometriotic growth and inflammation (Chantalat et al. 2020). Studies on E2 concentration in endometriotic lesions suggest that the aberrant estrogen regulation is localized to the proliferating cells, and is not reflective of circulating hormonal levels (Huhtinen et al. 2012). As a result, levels of estrogen have been used as a means to assist in diagnosis of endometriosis, but also as a pathway for treatments to target (Pavone et al. 2012). These high levels may be due to an increased expression of the enzyme aromatase, which catalyzes the conversion of androgens to estrogens, leading to an increased production of E2 (Kitawaki et al. 2002). Increased levels of E2 results in activation of the two estrogen receptors, ERα and ERβ, which are two subtypes of the steroid/nuclear receptor superfamily and act as transcriptional activators (Klinge et al. 2001). The binding of E2 and estrogen receptors induces a conformational change, forming a ligand-occupied estrogen receptor dimer, capable of transcriptional activation by binding estrogen response elements (EREs), 13 base-pair palindromic sequences upstream of genes that act as enhancers for transcriptional activation (Klinge et al. 2001). It is suggested that this activation of estrogen receptors, especially ERα, and the subsequent gene activation is responsible for an increase in cell proliferation and inflammation pathways (Chantalat et al. 2020).

    Synthetic Circuit for Detecting E2 Levels

    As E2 has been identified as a target for the diagnosis and the treatment of endometriosis, our team wanted to design a method, using synthetic biology, to determine concentrations of E2 from a tissue sample. The purpose of our synthetic circuit is to generate a biosensor for E2 levels to aid in endometriosis diagnosis and dosage of the several clinical treatments for endometriosis that target this pathway (Chantalat et al. 2020). This work was inspired by previous research and iGEM teams that generated synthetic circuits to detect levels of steroid hormones through the binding of ERα and ERβ and the subsequent activation of hormone responsive DNA sequences to activate expression of a fluorescent or luminescent reporter molecule (Leskinen et al. 2005, MIT iGEM 2016). Additionally, there has been success in generating a full-length estrogen receptor in Escherichia coli and has determined molecules that reduce toxicity and increase estrogen response element binding affinity of the receptor, and our work has been designed using synthetically-derived estrogen receptors in mind (Zhang et al. 2000). Our circuit expands upon this previous work by incorporating synthetic estrogen receptors, and to use the circuit to quantify E2 levels from the original sample for clinical use. Our circuit works by adding a patient sample of E2, from menstrual effluent, to a culture of our modified E. coli, where it will bind to estrogen receptors, derived and purified from a secondary bacteria, and activate an estrogen response element upstream of a T7 promoter. Activation of the estrogen response element will activate transcription of a gene coding for green fluorescent protein (GFP), and the fluorescence can then be measured so that the sample E2 concentration can be determined. A mathematical model was created to measure the relationship between GFP expression and E2 concentrations, and was used to optimize our circuit design to generate a detectable and non-toxic level of GFP within clinically relevant E2 ranges. The description of the design elements for the circuit can be found in the modeling section of our wiki. The part for our BioBrick BBa_K3346011 can be found on the parts page.

  • Design and Results
  • Design

    We designed experiments to test the efficiency of our UV sterilizer for the cleaning of our specially designed menstrual cup and the set up of our lateral flow assay.

    Menstrual Cup Sterilizer

    Menstrual Cup

    The goal of this series of experiments was to identify the most efficient methods for cleaning a menstrual cup. We hoped that these experiments would provide information on how efficient UV sterilizers are for cleaning a menstrual cup compared to the standard of boiling water, and how we could possibly improve the design of our UV sterilizer. We inoculated a menstrual cup with exponential-phase E. coli and determined the efficiency of various cleaning methods for eliminating bacteria on the surface of the menstrual cup. We tested a commercial UV sterilizer and boiling water since these methods are commonly recommended for the sanitization of menstrual cups.

    Results of Menstrual Cup Sterilization

    Each menstrual cup was inoculated with exponential growth-phase E. coli and allowed to dry in a 37 degrees Celsius incubator. The menstrual cups were then treated with a sanitization method. Efficiency of the sanitization method was determined by analyzing the colony forming units per millileter (CFU/mL) that remained on the menstrual cup after treatment compared to the untreated condition. We used two different sanitizing methods for this experiment: submerging the cup in boiling water for 5 minutes (the standard cleaning method) and using a commercially purchased UV sterilizer.
    These methods demonstrated that placing the cup in boiling water for 5 minutes yielded approximately 0 CFU/mL, while we saw a 0.19-log decrease in CFUs with the UV sterilizer compared to the untreated condition when used as instructed by the manufacturer (cup was enclosed in the device for 2 minutes) (Figure 1).

    Figure 1. Comparison of sanitization conditions. The no treatment condition had approximately 170,000 CFU/mL, the UV sterilizer treatment had 110,000 CFU/mL, and the boiling water condition had 0 CFU/mL with a lower sensitivity threshold of 1/10,000 CFU/mL. These values were then log transformed and plotted. The standard deviation for the untreated condition was +/- 0.14 LOG(CFU) and the standard deviation for both the boiling water and UV sterilizer conditions was +/- 0.00 LOG(CFU).

    As a result of this experiment, we wanted to figure out why such a low log-fold decrease of CFUs was observed when the menstrual cup was treated with UV light. Our hardware team had performed an in-depth literature review which suggested that UV light should be an efficient method for the sanitization of menstrual cups. Specifically, we wanted to test if increasing the time that the cup was exposed to the UV light would yield better results.

    Figure 2. The efficiency of the UV sterilizer at different durations of exposure. The UV sterilizer allowed for sanitization in 2-minute intervals. At 6 minutes and 10 minutes, no significant difference was observed for the amount of bacteria on the surface of the cups. The standard deviation was +/- 0.15 LOG(CFU) for 0 minutes exposure to UV light, +/- 0.30 LOG(CFU) for 6 minutes exposure to UV light, and +/- 0.25 LOG(CFU) for 10 minutes exposure to UV light.

    This experiment demonstrated that the commercially purchased UV sterilizer was ineffective for sanitizing the surface of the menstrual cup. Initially we thought that this might be due to low voltage, however, our hardware team determined that there was only a 7.1% decrease in voltage since we had purchased the sterilizer. As such, we hypothesize that the light might not be reaching all surfaces of the cup or the intensity of the light is not sufficient to kill bacteria on the surface of the cup. To address this issue, our hardware team decided to increase the number of UVC lights inside our sterilizer design and increase the voltage to the cup to ensure that the light reaches the proper intensity. We would also like to test the commercial sterilizer to check the wavelength of the light, as wavelengths outside of the range of 260 nm to 280 nm likely would not result in killing of the bacteria on the surface of the cup.

    Lateral Flow Assay Construction

    Lateral Flow Assay

    We constructed a lateral flow assay closely following the protocol outlined by Teerinen et al. We spent time troubleshooting our design to yield the optimal signal for our diagnostic. First, we had to optimize the dimensions of the materials in our test strips to ensure that our sample would reach the test line. We tried different concentrations of sample and lengths of sample pad until we found a volume at which the sample pad could become saturated and the fluid could flow down the test strip at a reasonable rate (reach the end in less than 20 minutes)

    Results of Sample Pad Optimization

    Figure 3. Flow rate of our synthetic serum (0.25% Tween-20, 2% bovine serum albumin, and PBS) across our lateral flow assay. The Y-axis shows the distance traveled from the end of the conjugate pad (distance travelled across the nitrocellulose membrane) and the X-axis demonstrates the time. To closely follow the lateral flow assay as outlined in our protocols, we wanted a flow rate of approximately 170 cm per 4 seconds. We tested sample volumes of 50 uL, 80 uL, and 100 uL as was suggested in literature. The lateral flow assay set up with a 1.0 cm length sample and conjugate pad yielded the best results with a flow rate of approximately 125 seconds per 4 centimeters.

    As a result of these experiments, we decided to use a sample pad, conjugate pad, and absorbent pad length of 1.0 cm compared to our original design. We had also initially planned to use a sample volume of 50 uL, but after this series of experiments, we realized that a greater sample volume of 80 uL. Future directions include the construction of the lateral flow assay with the antibodies (these flow rate experiments were conducted using test strips constructed without the deposition of antibodies) to determine if the proper signal will appear at the test line with a sample volume of 80 uL and sample pad length of 1.0 cm.

    Figure 4. Flow rate of our synthetic serum across the test strip. Gold nanoparticle-conjugated antibodies were placed on the conjugate pad to allow for visual detection of the flow rate.

    Future directions include the construction of the lateral flow assay with the antibodies (these flow rate experiments were conducted using test strips constructed without the deposition of antibodies) to determine if the proper signal will appear at the test line with a sample volume of 80 uL and sample pad length of 1.0 cm.


    Bedaiwy, M. A. (2002). Prediction of endometriosis with serum and peritoneal fluid markers: a prospective controlled trial. Human Reproduction, 17(2), 426–431.

    Bougie, O., Yap, M. ., Sikora, L., Flaxman, T., & Singh, S. (2019). Influence of race/ethnicity on prevalence and presentation of endometriosis: a systematic review and meta‐analysis. BJOG: An International Journal of Obstetrics & Gynaecology, 126(9), 1104–1115.

    Frystyk, J., Grønbæk, H., Skjærbæk, C., Flyvbjerg, A., Ørskov, H., & Baxter, R. C. (1998). Developmental Changes in Serum Levels of Free and Total Insulin-Like Growth Factor I (IGF-I), IGF-Binding Protein-1 and -3, and the Acid-Labile Subunit in Rats**This work was supported by grants from the Danish Health Research Council (Grants 9602012 and . Endocrinology, 139(10), 4286–4292.

    Galo, S., Zúbor, P., Szunyogh, N., Kajo, K., Macháleková, K., Biringer, K., & Visnovský, J. (2005). [TNF-alpha serum levels in women with endometriosis: prospective clinical study]. Ceska Gynekologie, 70(4), 286–290.

    Hirsch, M., Duffy, J. M. N., Davis, C. J., Nieves Plana, M., & Khan, K. S. (2016). Diagnostic accuracy of cancer antigen 125 for endometriosis: a systematic review and meta-analysis. BJOG: An International Journal of Obstetrics and Gynaecology, 123(11), 1761–1768.

    Juul, A., Scheike, T., Pedersen, A. T., Main, K. M., Andersson, A. M., Pedersen, L. M., & Skakkebaek, N. E. (1997). Changes in serum concentrations of growth hormone, insulin, insulin- like growth factor and insulin-like growth factor-binding proteins 1 and 3 and urinary growth hormone excretion during the menstrual cycle. Human Reproduction, 12(10), 2123–2128.

    Karimi-Zarchi, M., Dehshiri-Zadeh, N., Sekhavat, L., & Nosouhi, F. (2016). Correlation of CA-125 serum level and clinico-pathological characteristic of patients with endometriosis. International Journal of Reproductive Biomedicine (Yazd, Iran), 14(11), 713–718.

    Lobstein, J., Emrich, C. A., Jeans, C., Faulkner, M., Riggs, P., & Berkmen, M. (2012). SHuffle, a novel Escherichia coli protein expression strain capable of correctly folding disulfide bonded proteins in its cytoplasm. Microbial Cell Factories, 11, 56.

    Malutan, A. M., Drugan, T., Costin, N., Ciortea, R., Bucuri, C., Rada, M. P., & Mihu, D. (2015). Clinical immunology Pro-inflammatory cytokines for evaluation of inflammatory status in endometriosis. Central European Journal of Immunology, 1, 96–102.

    Malvezzi, H., Hernandes, C., Piccinato, C. A., & Podgaec, S. (2019). Interleukin in endometriosis-associated infertility-pelvic pain: Systematic review and meta-analysis. Reproduction, 158(1), 1–12.

    Nayyar, A., Saleem, M. I., Yilmaz, M., De Franco, M., Klein, G., Elmaliki, K. M., Kowalsky, E., Chatterjee, P. K., Xue, X., Viswanathan, R., Shih, A., Gregersen, P. K., & Metz, C. N. (2020). Menstrual Effluent Provides a Novel Diagnostic Window on the Pathogenesis of Endometriosis. Frontiers in Reproductive Health.

    Ohata, Y., Harada, T., Miyakoda, H., Taniguchi, F., Iwabe, T., & Terakawa, N. (2008). Serum interleukin-8 levels are elevated in patients with ovarian endometrioma. Fertility and Sterility, 90(4), 994–999.

    Okada, H., Tsuzuki, T., & Murata, H. (2018). Decidualization of the human endometrium. Reproductive Medicine and Biology, 17(3), 220–227.

    Perez, B. H., & Gipson, I. K. (2008). Focus on Molecules: Human mucin MUC16. Experimental Eye Research, 87(5), 400–401.

    Robinson, M.-P., Ke, N., Lobstein, J., Peterson, C., Szkodny, A., Mansell, T. J., Tuckey, C., Riggs, P. D., Colussi, P. A., Noren, C. J., Taron, C. H., DeLisa, M. P., & Berkmen, M. (2015). Efficient expression of full-length antibodies in the cytoplasm of engineered bacteria. Nature Communications, 6(1), 8072.

    Sabbaj, S., Hel, Z., Richter, H. E., Mestecky, J., & Goepfert, P. A. (2011). Menstrual blood as a potential source of endometrial derived CD3+ T cells. PLoS ONE, 6(12), 4–12.\

    Sajid, M., Kawde, A.-N., & Daud, M. (2015). Designs, formats and applications of lateral flow assay: A literature review. Journal of Saudi Chemical Society, 19(6), 689–705.

    Teerinen, T., Lappalainen, T., & Erho, T. (2014). A paper-based lateral flow assay for morphine. Analytical and Bioanalytical Chemistry, 406(24), 5955–5965.

    The Humane Society of the United States (2005). Pain and Distress Associated with Polyclonal Antibody Production: Discussion and Recommendations.

    Warren, L. A., Shih, A., Renteira, S. M., Seckin, T., Blau, B., Simpfendorfer, K., Lee, A., Metz, C. N., & Gregersen, P. K. (2018). Analysis of menstrual effluent: Diagnostic potential for endometriosis. Molecular Medicine, 24(1), 1–12.

    Chantalat, E., Valera, M.-C., Vaysse, C., Noirrit, E., Rusidze, M., Weyl, A., . . . Lenfant, F. (2020). Estrogen Receptors and Endometriosis. International journal of molecular sciences, 21(8), 2815. doi:10.3390/ijms21082815

    Huhtinen, K., Desai, R., Stahle, M., Salminen, A., Handelsman, D. J., Perheentupa, A., & Poutanen, M. (2012). Endometrial and endometriotic concentrations of estrone and estradiol are determined by local metabolism rather than circulating levels. The Journal of clinical endocrinology and metabolism, 97(11), 4228-4235. doi:10.1210/jc.2012-1154

    Kitawaki, J., Kado, N., Ishihara, H., Koshiba, H., Kitaoka, Y., & Honjo, H. (2002). Endometriosis: the pathophysiology as an estrogen-dependent disease. The Journal of Steroid Biochemistry and Molecular Biology, 83(1), 149-155. doi:10.1016/S0960-0760(02)00260-1

    Klinge, C. M. (2001). Estrogen receptor interaction with estrogen response elements. Nucleic acids research, 29(14), 2905-2919. doi:10.1093/nar/29.14.2905

    Leskinen, P., Michelini, E., Picard, D., Karp, M., & Virta, M. (2005). Bioluminescent yeast assays for detecting estrogenic and androgenic activity in different matrices. Chemosphere, 61(2), 259-266. doi:10.1016/j.chemosphere.2005.01.080

    MIT 2016 iGEM (2016). Diagnosing Endometriosis. iGEM Wiki,

    Pavone, M., Bulun, S. (2012). Aromatase inhibitors for the treatment of endometriosis. Fertility and Sterility, 98(6), 1370-1379. doi:10.1016/j.fertnstert.2012.08.053.

    Zhang, C. C., Glenn, K. A., Kuntz, M. A., & Shapiro, D. J. (2000). High level expression of full-length estrogen receptor in Escherichia coli is facilitated by the uncoupler of oxidative phosphorylation, CCCP. The Journal of Steroid Biochemistry and Molecular Biology, 74(4), 169-178. doi:10.1016/S0960-0760(00)00120-5