Background
It is well known that the primary prevention of disease is extremely important. However, the current diagnostic methodologies, which often involve techniques such as Next-Generation Sequencing (NGS), real-time fluorescence quantification PCR, ELISA, etc. are invariably time-intensive, expensive, need specialized technicians and are case-specific[1], i.e. these techniques tend to be tailored to detect particular biomarkers, which are the biological indicators of specific diseases.
Fig.1 Pictures showing two methodologies applied in hospitals and one unknown but new methodology developed by our team. Pictures are from network.
Project Description
Here, we come up with an idea combining synthetic biology and diagnostics — GPA, short for General diseases detection with Protease and Aptamer, to create a new methodology for disease detection featuring on a shorter working time, lower cost, user-friendliness, and most importantly, modularization and generalization.
The overall design of our system can be divided into three parts. First, biomarker recognition part. Second, signal amplification part[2]. Third, signal output part. In our system, there are two single stranded nucleotides (called aptamers) linked to a protein (called split protease) respectively. If a person was diagnosed with certain kind of disease, the sample taken from him/her would have a specific biomarker of this kind. And this biomarker would be recognized by two aptamers, thus the two aptamers would be apposed to each other and subsequently resulting in juxtaposition of two proteins linked to the aptamers. Additionally, juxtaposition of two proteins would form an activated protease, allowing the protease to cut the downstream sequence and activate another protease. The two activation events form a cascade to amplify the signals detected by aptamers. Finally, the signal would be demonstrated through gold colloidal kit. It’s notable that due to time limitation, we will only use thrombin as the target molecule recognized by aptamers. Nevertheless, we will compensate for this limitation in dry lab (discussed later).
Fig.2 The overall design of our GPA system.
The generalization and modularization of our system are embodied in the fact that this system can detect various kinds of diseases by only changing the aptamer module of our system. Generalization is realized by an AI tool developed by our team, which is able to generate the optimal aptamers to bind given biomarkers. Modularization is supported by the different properties of nucleotides and proteins, for the structures of nucleotides are more flexible while structures of proteins are more rigid, thus the nucleotide module being variable in our system while the protein module being constant. Plus, the cost of nucleotides production is lower than that of proteins.
Fig.3 Pictures emphasizing the modularization of our system.
Application
Gold colloidal kit
Featuring on a shorter working time, lower cost and user-friendliness, our system best suits the application scenario when people don’t bother to go to hospital for disease diagnosis and just do it at home. Futhermore, because of the amplification effect brought by enzyme reaction, our system is qualitative and less likely to be a quantitative system, which further supports the application of this system in gold colloidal kit.
Fig.4 Schematic diagram of gold colloidal kit, adapted from network.
Large-scale disease detection
This application is inspired by a communication with a biotechnology company. Experienced in antibody production, they told us that in diagnosis of a common kind of disease, antibody still has its competitiveness for its mature production process and its stability for storage. However, if the quantity of biomarkers is large (much more than one), for example, biomarkers of a heterogeneous disease, our system has significant advantage over antibody. Because it is faster, easier and cheaper to generate new aptamers tailored to different biomarkers.
Fig.5 Schematic diagram of large-scale disease detection, taken Alzheimer Disease as an example, obtained from network.
References
[1] Ting Wang, Ziwei Wang, Linlin Bai, Xingcai Zhang, Jia Feng, Cheng Qian, Yongming Wang, Rui Wang, Next-generation CRISPR-based diagnostic tools for human diseases, TrAC Trends in Analytical Chemistry, Volume 168, 2023.
[2] Fink T, Lonzarić J, Praznik A, Plaper T, Merljak E, Leben K, Jerala N, Lebar T, Strmšek Ž, Lapenta F, Benčina M, Jerala R. Design of fast proteolysis-based signaling and logic circuits in mammalian cells. Nat Chem Biol. 2019 Feb;15(2):115-122.