Project Description
The management of chronic diseases is extending from middle-aged and elderly populations to younger age groups. We have observed that the "Four Highs"—hypertension, hyperlipidemia, hyperglycemia, and high body weight (overweight and obesity)—have become the leading causes of coronary heart disease (CHD) deaths worldwide [1]. Data also indicates that CHD-related deaths have shifted from developed to developing countries. The most effective and cost-efficient strategy to reduce CHD-related mortality is the early adoption of preventative measures across the entire population. This involves effectively implementing interventions targeting various risk factors, including controlling hypertension, hypercholesterolemia, hyperglycemia, and obesity, which require long-term monitoring and timely intervention [2].
The prevention of chronic diseases relies heavily on real-time monitoring and timely intervention, with home use being the most common scenario. We are dedicated to developing a high-precision biosensor capable of detecting multiple biomarkers [3-6]. Through standardized screening and optimization, this device aims to address the existing issues of low accuracy, frequent calibration requirements, and limited functionality when detecting high uric acid, high blood glucose, depression, and lactic acid buildup [7, 8].
To validate its functionality, we have selected blood glucose and uric acid as the initial biomarkers for testing. The concentration of blood glucose and uric acid in samples will be indicated by fluorescence intensity. In the early stages of our HP (Human Practices) process, we explored the needs for home medical testing devices and identified additional meaningful detection targets, such as lactic acid and neurotransmitters related to depression. To enhance the practicality of the project, we will also design a simple detection device to demonstrate our detection principles and application scenarios, utilizing microfluidic technology to achieve rapid, simultaneous detection of multiple indicators.
Fig. 1 Biosensor detection pathway. Using uric acid detection as an example, a simulated detection system operates as follows: Transcription Unit 1 transports the target molecule into the cell while simultaneously triggering the expression of the HucR protein. The activity of the HucR protein is influenced by the concentration of uric acid, which in turn affects the function of HucO. This modulation impacts the expression level of the fluorescent protein in Transcription Unit 2, thereby enabling detection..
[1] Wang, W., Hu, M., Liu, H., Zhang, X., Li, H., Zhou, F., Liu, Y. M., Lei, F., Qin, J. J., Zhao, Y. C., Chen, Z., Liu, W., Song, X., Huang, X., Zhu, L., Ji, Y. X., Zhang, P., Zhang, X. J., She, Z. G., Yang, J., … Li, H. (2021). Global Burden of Disease Study 2019 suggests that metabolic risk factors are the leading drivers of the burden of ischemic heart disease. Cell metabolism, 33(10), 1943–1956.e2. https://doi.org/10.1016/j.cmet.2021.08.005
[2] Krouwer JS, Cembrowski GS. A Review of Standards and Statistics Used to Describe Blood Glucose Monitor Performance. Journal of Diabetes Science and Technology. 2010;4(1):75-83. doi:10.1177/193229681000400110
[3] https://2019.igem.org/Team:QHFZ-China
[4] https://2017.igem.org/Team:Hong_Kong_UCCKE
[5] Wilkinson, S. P., et al. (2004). HucR, a Novel Uric Acid-responsive Member of the MarR Family of Transcriptional Regulators from Deinococcus radiodurans. Journal of Biological Chemistry, 279(49), 51442-51450. https://doi.org/10.1074/jbc.M409360200
[6] Rho, S., Jung, W., Park, J. K., Choi, M. H., Kim, M., Kim, J., Byun, J., Park, T., Lee, B. I., Wilkinson, S. P., & Park, S. (2022). The structure of Deinococcus radiodurans transcriptional regulator HucR retold with the urate bound. Biochemical and Biophysical Research Communications, 615, 63-69. https://doi.org/10.1016/j.bbrc.2022.05.034
[7] King, F., Ahn, D., Hsiao, V., Porco, T., & Klonoff, D. C. (2018). A Review of Blood Glucose Monitor Accuracy. Diabetes Technology & Therapeutics. https://doi.org/10.1089/dia.2018.0232
[8] Guo, J., & Ma, X. (2017). Simultaneous monitoring of glucose and uric acid on a single test strip with dual channels. Biosensors and Bioelectronics, 94, 415-419. https://doi.org/10.1016/j.bios.2017.03.026