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.

Emerging plant disease and pest outbreaks reduce food security, national security, human health, and the environment, with serious economic implications for North Carolina growers. These outbreaks may accelerate in coming decades due to shifts in the geographic distributions of pests, pathogens and vectors in response to climate change and commerce. Data-driven agbioscience tools can help growers solve pest and disease problems in the field more quickly but there is an urgent need to harness game-changing technologies. Computing devices are now embedded in our personal lives with sensors, wireless technology, and connectivity in the ����������������Internet of Things��������������� (IoT) but these technologies have yet to be scaled to agriculture. Our interdisciplinary team will build transformative sensor technology to identify plant pathogens, link local pathogen data and weather data, bioinformatics tools (pathogen genotypes), and use data driven analytics to map outbreaks, estimate pest and pathogen risk and economic damage, in order to coordinate response to emerging diseases, and contain threats. Sensor-supported early and accurate detection of pathogens before an outbreak becomes wide-spread in growing crops will significantly reduce pesticide use and increase crop yields.

Challenges at the FEW nexus are not simply technological, but convergent in the sense of spanning technical, ecological, social, political, and ethical issues. The field of biotechnology is evolving rapidly - and with it, the potential for creating a diverse array of powerful future products that could intentionally and unintentionally impact FEW systems. Depending on what products are developed and how those products are deployed, biotechnology could have a positive or negative impact on all 3 of these systems. Wise decisions will require leaders who can integrate knowledge from engineering, design, natural sciences, and social sciences. We will train STEM graduate students to respond to these challenges by conducting convergent research aimed at development, and assessment of biotechnologies to improve services provided by FEW systems. We will train our students to engage with non-scientists to elevate societal discourse about biotechnology. We will recruit 3 cohorts with emphasis on students who have shown a passion for crossing between natural and social sciences. We will work with the NCSU Initiative for Maximizing Student Diversity in recruiting students from underrepresented minority groups. Cohorts will have 6 students who will take a minor in Genetic Engineering and Society (GES). They will receive PhDs in established graduate programs such as Plant Biol, Chem & Biomol Engr, Econ, Public Adm, Entomol, Plant Path, Communication, Rhetoric & Digital Media, Forestry & Environ Res, Crop & Soil Sci, and Genetics. For students in natural science PhD programs, at least 1 thesis committee member will be from a social sciences program and vice versa for students in social sciences. For all students, at least 1 thesis chapter will demonstrate scholarship across natural and social sciences. The disciplinary breadth of our proposed NRT is very broad, so we will focus student projects narrowly on a specific biotechnology product that impact FEW systems. When they first arrive at NCSU, cohorts will participate in a training program off campus where they will be exposed to the issues they will address. Students will carry out a group project in the focus area of the cohort to continue team development. To fulfill the GES minor, students will take 3 specially designed courses: Plant Genetics & Physiology, Science Communication & Engagement, Policy & Systems Modeling. There are no NRT-eligible institutions partnering on this project outside of an evaluation role.

This is year three funding suggestion for USDA APHIS funding. In this project renewal, we will deploy in-field volatile organic compound (VOC) sensors and microneedle patch-supported LAMP sensors that can differentiate several important Phytophthora species of regulatory concern including 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 are developing species-specific LAMP and VOC sensors and will deploy these sensors with inexpensive cartridges to run on a smartphone reader.

Emerging plant disease outbreaks cost the US billions of dollars in loss of crop yields, loss of species diversity, and control and mitigation measure costs. We are developing faster and more reliable in-field detection methods that will greatly reduce this cost by reducing time for occurrence to detection and thus time to action. We have developed two new innovations in sensor technology including a field-compatible molecular assay that uses loop-mediated isothermal amplification (LAMP) and volatile-based sensors that will speed identification of plant pathogens in the field. In this project renewal, we will deploy in-field volatile organic compound (VOC) sensors and microneedle patch-supported LAMP sensors that can differentiate several important Phytophthora species of regulatory concern including 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 are developing species-specific LAMP and VOC sensors and will deploy these sensors with inexpensive cartridges to run on a smartphone reader. We have generated Phytophthora sequence data from field samples and previously published archived sequence databases to create an open buildable phylogeny of ����������������Emerging Phytophthora���������������. Pathogen occurrence data will be used to display 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 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 pathogen threats and improve economic return of growers as they use the digital diagnostic tools to prevent the spread of important Phytophthora diseases.

Crop production and disease protection present global concerns in every region of the world. Current diagnostic methods of plant diseases are heavily focused on genetic molecular assays (e.g., PCR) or immunological biosensors (e.g., antibody-based lateral flow assay or ELISA), most of which are time-consuming and invasive for sample preparation, subject to instability of reagents, and lack of an integrated framework for on-site data analysis and sharing. On the other side, there is an increasing need for rapid, noninvasive, yet highly cost-effective and connected sensors which can identify multiple infectious species simultaneously in the crop field and monitor disease outbreaks spatiotemporally. Here, we propose to develop a transformative smartphone-based optical sensing platform that enables the early diagnosis of plant diseases (e.g., potato late blight) caused by associated fungal or bacterial infections, based on the identification of characteristic volatile organic compounds (VOC) released from different plant disease models using a disposable chemo-responsive nanoplasmonic sensor array combined with multimodal smartphone readout (brightfield + fluorescence). This project is expected to provide an economically vital, field-deployable, and noninvasive solution for early diagnosis of various important plant diseases or monitoring of abiotic stresses with a high degree of detection sensitivity and specificity.