An Enzyme Self-Amplification System for Ultrasensitive Detection of Biomarkers at the Point of Care

项目总结 在护理时快速、廉价地检测生物标记物对于许多临床目的是至关重要的。然而， 目前检测平台的局限性阻碍了对许多蛋白质和小分子的灵敏检测 分子生物标志物，迫使临床医生要么依赖潜在的不准确的经验诊断，要么依赖昂贵的实验室 用于做出关键治疗决定的测试。对核酸靶标的灵敏检测已经很容易实现 通过利用Watson-Crick碱基配对来放大信号(PCR、LAMP、Cas9等)，但一直缺乏 在护理点检测低浓度抗原和小分子方面的创新。生物学有 通过翻译后修饰在体内快速放大蛋白质信号的进化复杂机制 和基于蛋白质的信号网络。朝着开发新的、快速的、超灵敏的诊断的目标， 该项目的中心假设是，在体外，基于蛋白质的信号网络结合了自我 放大酶途径将导致具有无与伦比的传感的生物标记物检测平台 能力。具体地说，我们计划研究蛋白质信号网络的两种机制，这些机制可能 诊断：裂解酶重组和自催化正反馈循环。首先，我们将调查 体外应用裂解腺苷环化酶检测小分子。分析物的检测将是 通过同时结合两个蛋白质(即溶液中的三明治试验)来完成，带来两个 两半腺苷环化在一起，产生cAMP。第二，我们将调查分裂的融合 腺苷环化酶和cAMP受体蛋白在体外创建自催化反馈环路。此循环将 通过生产更多的营地来回应营地。最后，我们将开发基于常微分方程式的 用于理解和设计诊断属性的模型。这些蛋白质信号网络的动态模型 将由测量的实验参数来告知。这些模型将用于创建组合的 建立了高灵敏度、快速的小分子传感器模型，为以后的工作提供了理论依据。如果成功，这将是 该系统将广泛适用于蛋白质和小分子检测，并可用于检测 具有已知抗体结合域的广泛的目标分析物。因此，该系统可以用作 用于检测目前无法快速检测的多种蛋白质和小分子分析物的平台 在看护的时候。在整个项目过程中，该研究员将接受合成生物学方面的技术培训 方法、蛋白质工程和动力学计算建模，以及教学中的职业培训 和导师最佳实践、手稿准备、资助金和研究交流 赞助商和共同赞助商，以及西北大学研究所提供的资源。此外， 学员将有机会建立一个强大的合成生物学家、诊断设计专业网络 专家和全球健康临床医生在她的培训过程中，并将继续制定建议的 将检测平台转化为临床可翻译的诊断仪器作为独立研究人员。

PROJECT SUMMARY Rapid, inexpensive detection of biomarkers at the point of care is vital for many clinical purposes. However, limitations in current detection platforms have prevented the sensitive detection of many protein and small molecule biomarkers, forcing clinicians to rely either potentially inaccurate empirical diagnosis or expensive lab tests to make critical treatment decisions. Sensitive detection of nucleic acid targets has been readily achieved by exploiting Watson-Crick base pairing to amplify signals (PCR, LAMP, Cas9, etc.), but there has been a lack of innovation for detection of low concentration antigens and small molecules at the point of care. Biology has evolved intricate mechanisms for rapidly amplifying protein signals in vivo via post-translational modification and protein based signaling networks. Towards the goal of developing novel, rapid, ultrasensitive diagnostics, the central hypothesis of this project is that in vitro, protein-based signaling networks incorporating self- amplifying enzymatic pathways will result in biomarker detection platforms with unparalleled sensing capabilities. Specifically, we plan to investigate two mechanisms of protein signaling networks with potential for diagnostics: split enzyme reconstitution and autocatalytic positive feedback loops. First, we will investigate the in vitro use of split adenylate cyclase for small molecule detection. Detection of the analyte will be accomplished by the simultaneously binding two proteins (i.e. a sandwich assay in solution), bringing two halves of adenylate cyclase together and producing cAMP. Second, we will investigate fusions of split adenylate cyclase and cAMP receptor protein to create an autocatalytic feedback loop in vitro. This loop will respond to cAMP by producing more cAMP. Finally, we will develop ordinary differential equation-based models to understand and engineer diagnostic properties. Dynamic models of these protein-signaling networks will be informed by measured experimental parameters. These models will be used to create a combined model for a high sensitivity, fast small molecule sensor as a proof-of-principle for future work. If successful, this system would be broadly applicable for protein and small molecule detection and could be used to detect a wide range of target analytes with known antibody binding domains. As such, this system could be used as a platform for the detection of many protein and small molecule analytes currently unable to be rapidly detected at the point of care. Over the course of the project the fellow will receive technical training in synthetic biology methods, protein engineering, and kinetics computational modeling, in addition to career training in teaching and mentorship best practices, manuscript preparation, grantsmanship, and research communication from the sponsor and co-sponsor and resources available through institutes at Northwestern University. Additionally, the trainee will have the opportunity build a strong professional network of synthetic biologists, diagnostic design experts, and global health clinicians over the course of her training and will continue developing the proposed detection platform into clinically translatable diagnostic devices as an independent researcher.