Jean Ristaino

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.

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.

Project is in support of PSI. We have developed faster and more reliable in-field detection methods for plant pathogens that will greatly reduce plant disease by reducing time from occurrence to detection and thus time to mitigation. Two new innovations in sensor technology have been developed including a smart-phone field-compatible molecular assay that uses a loop-mediated isothermal amplification (LAMP) sensor and a volatile-based sensor that will speed identification of plant pathogens in the field. In this project renewal, we will continue deploy and field test work a volatile organic compound (VOC) sensor and microneedle patch-supported LAMP sensors to differentiate two regulatory Phytophthora species of concern, P. ramorum and P. kernoviae. Phytophthora ramorum and P. kernoviae cause disease on nursery plants such as rhododendron, lilac and kalmia and important forestry tree species including oak and beech among others. Phytophthora kernoviae has not yet been found in the US. We will test the sensors in field tests and deploy them with inexpensive cartridges to run on a smartphone reader. We will also complete the modeling of historic late blight disease occurrence data using a near-real time mapping platform and the process based spatially explicit discrete time PoPS (Pest or Pathogen Spread) Forecasting Platform to develop predictive maps of pathogen risk of spread at regular intervals. The system will improve the response time of USDA APHIS PPQ and National Plant Diagnostic Network (NPDN) personnel to respond to emerging Phytophthora threats and improve economic return of growers as they use the digital diagnostic tools to prevent the spread of important Phytophthora diseases.

Emerging plant disease and pest outbreaks reduce crop production with serious economic implications for North Carolina growers. We have developed cost effective molecular dignostic assays that can reduce time to indentification of Phytophthora diseases on potato ans tomato. They can also help target fingicde application when combined with decision support tools. In this project we will deploy a set of smart phone based diagnostic sensors in field tests in western NC on to and in astern NC on potato. Faster more reliable methods of pathogen detection could greatly reduce control costs by reducing time to detection and thus, time to action.