Qingshan Wei

In this proposal, we aim to study and develop a transformative plant wearable sensor that can be deployed on-plant for continuous monitoring of biotic and abiotic stresses of plants and their microenvironment to inform plant health status and early detection of plant diseases. This multifunctional plant wearable sensor will include an array of ligand-functionalzied chemiresistive sensors to profile plant leaf VOCs and nanowire-based flexible sensors to monitor microclimate in parallel. The sensors will be prepared on a light-transparent, gas-permeable, and stretach substrate for long-term wearibility on live plants. In addition, a signal transmitter will be developed for wireless data acquistion and transmission. The system will be thourughly tested on tomato plants in the greenhouse for stress monitoring and disease detection.

The overarching goal of this project is to systematically study and optimize two microneedle-based platforms for rapid DNA extraction and genotyping from plant leaves and seeds, respectively. DNA genotyping is an indispensable tool to identify specific traits and select progeny in plant breeding. However, the current seed genotyping method is a complicated multistep process, involving seed chipping, DNA extraction, and assaying. On the other side, leaf genotyping is relatively simpler, but it depends on manual punctuation of leaf tissues and actual breeding of new crop species before analysis, which increases both time and test cost significantly. To address these immediate needs, our team will investigate a novel plant DNA extraction and genotyping system that is robust, simple, and scalable for single-nucleotide polymorphism (SNP) analysis for both plant leaves and seeds. Two DNA extraction platforms, namely the polymeric microneedle array (PMA) and metallic microneedle (MM), will be developed and optimized for leaf and seed DNA isolation, respectively. The extraction system will be integrated with a multiplexed genotyping assay such as padlock-based rolling circle amplification (RCA) for rapid detection of specific trait loci markers. The potential for on-needle detection of SNPs and automation of the entire process will also be explored.

This CAREER proposal seeks to study the fundamental properties of CRISPR Cas proteins for nucleic acid detection through collateral nonspecific cleavage (or trans-cleavage). Systematic characterization, optimization, and control of the enzyme activity and kinetics of Cas proteins will convert genome-editing CRISPR-Cas platform into next-generation, rapid, and ultrasensitive biosensors. However, the detailed mechanism and properties of trans-cleavage are still not fully understood yet. Moreover, many existing CRISPR biosensors require pre-amplification steps to achieve high detection sensitivity, which significantly hinders their point-of-care (POC) applications. Our recent data (see Section RO1) suggest that trans-cleavage kinetics of Cas proteins in a one-pot reaction is different from the literature reports of pre-assembled and activated Cas-crRNA complex. Therefore, a revised enzymatic model is needed to accurately describe the enzymatic properties of CRISPR biosensor. As such, the overarching goal of this work is to understand and control the unique characteristics of CRISPR trans-nuclease and use the knowledge gained to design a chip-based, preamplification-free digital CRISPR (dCRISPR) sensor chip. The sensor chip will be coupled with a newly designed smartphone scope, EpiView, to form a cost-effective, smartphone-based testing platform for POC measurement of viral load of the human immunodeficiency virus (HIV) from finger prick blood.

Breast cancer (BC) is a major global health concern. It is the most common cancer among women and leading cause of cancer death for women worldwide. Limited-resource settings (LRS) account for about half of the cases and majority of deaths from BC, and rates are increasing. The reason for poor outcomes in LRS relative to abundant-resource settings (ARS) is related to higher incidence of late-stage presentation resulting from the lack of healthcare infrastructure to support diagnostic pathology services for BC. Breast pathology is the cornerstone of appropriate BC management, and the quality of pathology services directly correlates to the quality of care and patient outcomes. Unfortunately, access to adequate breast pathology services can be limited or even nonexistent in LRS. The objective of proposal is to develop a low-cost, mobile platform that provides cellular and molecular breast pathology of BC. This proposal is significant as it directly address the unmet need for sustainable approaches toward complete breast pathology in LRS and has the potential for transformative impact by improving survival of BC patients in LRS worldwide.

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world and in the US. Climate change is exacerbating weather events that affect crop production and food access for vulnerable areas. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover and evolution of new pathogen genetic lineages. Prediction of plant disease pandemics is unreliable due to the lack of real-time detection, surveillance and data analytics to inform decisions and prevent spread. In order to tackle these grand challenges, a new set of predictive tools are needed. In the PIPP Phase I project, our multidisciplinary team will develop a pandemic prediction system called ����������������Plant Aid Database (PAdb)��������������� that links pathogen transmission biology, disease detection by in-situ and remote sensing, genomics of emerging pathogen strains and real-time spatial and temporal data analytics and predictive simulations to prevent pandemics. We plan to validate the PAdb using several model pathogens including novel and host resistance breaking strains of lineages of two Phytophthora species, Phytophthora infestans and P. ramorum and the cucurbit downy mildew pathogen Pseudoperonspora cubensis Adoption of new technologies and mitigation interventions to stop pandemics require acceptance by society. In our work, we will also characterize how human attitudes and social behavior impact disease transmission and adoption of surveillance and sensor technologies by engaging a broad group of stakeholders including growers, extension specialist, the USDA APHIS, Department of Homeland Security and the National Plant Diagnostic Network in a Biosecurity Preparedness workshop. This convergence science team will develop tools that help mitigate future plant disease pandemics using predictive intelligence. The tools and data can help stakeholders prevent spread from initial source populations before pandemics occur and are broadly applicable to animal and human pandemic research.