Nucleic acid amplification tests (NAATs) are powerful tools for infectious disease diagnostics. While NAATs are routinely used in the clinic, their use in point-of-care (POC) contexts is constrained by complex procedures needed to extract and purify nucleic acids from patient samples. This is especially troublesome for RNA targets, due to the fragility of RNA and abundance of RNA-degrading ribonucleases in samples and the environment. Nevertheless, RNA viruses are key targets for POC diagnostics, as they are abundant in low- resource settings (e.g. low-income countries, where roughly 20 million HIV patients live) or in settings where fast turnaround and patient self-testing are useful (e.g. outbreaks of airborne viruses such as influenza and coronavirus). Thus, simpler approaches to RNA sample preparation are needed in POC-NAAT contexts. Much of the complexity of NAAT sample preparation stems from the paradoxical need to add chaotropes to extract nucleic acids, then remove chaotropes before amplification. Chaotropes, such as guanidinium thiocyanate (GuSCN), are chemical denaturants that disrupt the structure of biological macromolecules. They are used in NAAT sample preparation to lyse target virions/cells and denature inhibitors, such as ribonucleases and proteases. Chaotropes are effective, but also inhibit polymerase activity, so they must be removed before amplification. Many POC adaptations of NAAT workflows involve robotic or microfluidic automation of chaotrope addition and removal, but still require specialized equipment and/or laboratory resources. We will address the POC-NAAT sample preparation bottleneck in a different way: instead of automating chaotrope removal, we will eliminate the need for it. We propose to engineer a polymerase to be ?chaostable?, or active in a chaotropic amplification buffer, enabling simultaneous extraction, amplification, and detection of viral RNA targets in a single tube. To realize this vision, we propose three specific aims, using a starting polymerase previously developed by our lab and HIV-1 as a model RNA target.
Aim 1 : Develop a chaostable polymerase via compartmentalized self-replication in chaotropic conditions. We will use a high-throughput directed evolution approach to develop a polymerase that retains activity in 3M GuSCN (the minimum recommended concentration for RNA extraction buffers).
Aim 2 : Investigate mechanisms of polymerase chaotrope resistance via deep mutational scanning and molecular dynamics simulations. We will use deep mutational scanning and molecular dynamics simulations to study interactions of GuSCN on our polymerase and identify rational design methods for GuSCN resistance.
Aim 3 : Incorporate chaostable polymerase into a proof-of-concept HIV diagnostic that performs sample lysis, RT-LAMP amplification, and colorimetric detection in a single tube. Using the best-performing chaostable polymerases developed in Aims 1 and 2, we will design a single-tube RT-LAMP assay for HIV, and test it against pure HIV RNA and HIV patient plasma samples.

Public Health Relevance

Most nucleic acid tests use harsh denaturants to extract DNA or RNA from a patient sample, then use a polymerase enzyme to detect infectious diseases, genetic disorders, and different types of cancer. Because the denaturants can inhibit the polymerase, several intermediate purification steps are required to remove them before adding the polymerase, which increases assay complexity, time, and cost. We aim to engineer a polymerase to work in the presence of denaturants, so sample purification is not needed, and nucleic acid tests can be performed quickly and easily in low-resource healthcare settings.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21AI156017-01
Application #
10105591
Study Section
Enabling Bioanalytical and Imaging Technologies Study Section (EBIT)
Program Officer
Fitzgibbon, Joseph E
Project Start
2020-12-01
Project End
2022-11-30
Budget Start
2020-12-01
Budget End
2021-11-30
Support Year
1
Fiscal Year
2021
Total Cost
Indirect Cost
Name
University of Washington
Department
Engineering (All Types)
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195