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

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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.