Finding the Needle in a Haystack: Creating a qPCR Test to Locate a Threat to Sustainability Hiding Among Symptomless Strawberry Transplants

Commodities

Practices

Strawberries are a 3.4-billion-dollar specialty crop in the U.S., with Florida alone planting more than 14,000 acres each season. Neopestalotiopsis has emerged as a fungal pathogen that now threatens the sustainability of U.S. production, particularly within the southern region. Management is hampered by lack of visible symptoms on infected nursery plants, fostering distribution of the pathogen to fruit production fields where it can cause losses exceeding $100,000 per hectare, often before a single fruit is harvested. Desperate to stop epidemics, growers deploy multiple fungicide sprays, often applied in vain to contain the disease. This is an unsustainable, environmentally destructive, and costly endeavor that only adds to stakeholder losses. We urgently needed a proactive approach that enables pre-symptomatic detection of the pathogen. qPCR is a technique that is purpose-built for such use. The Peres program has sampling strategies and means to optimally deploy qPCR for pathogen detection, but in the case of Neopestalotiopsis, we lack the very tool for deployment. We propose the development and validation of a qPCR assay to detect Neopestalotiopsis: a test that avoids false positives from dead material and delivers biologically meaningful results. A tiered batch-sampling process successfully used in virus certification Covid-19 detection programs will be adapted as a scalable and practical way to screen the necessarily enormous volumes of transplants. This project directly aligns with Southern SARE's mission to promote sustainable solutions, reduce chemical inputs, improve plant health, and protect farm profitability.

The overarching goal of this project is to complete and validate a qPCR-based diagnostic protocol for detecting Neopestalotiopsis spp. in asymptomatic strawberry nursery plants. The pathogen's latent infection behavior presents a significant challenge to strawberry production in Florida and the Southern U.S., as infected but symptomless transplants can initiate severe disease outbreaks in fruiting fields. While initial steps of assay development have already begun, further validation and application are needed to ensure the tool's reliability and practical use in real-world settings. This proposal seeks funding to complete three critical objectives necessary for integrating this diagnostic approach into disease prevention systems at the nursery level.

The specific objectives of this project are:

Objective 1: Optimize and validate a qPCR assay for detecting Neopestalotiopsis in strawberry leaf tissue under controlled conditions.

Strawberry plants will be grown under controlled growth room conditions and inoculated with a gradient of Neopestalotiopsis conidial concentrations (10¹ to 104 conidia/mL). Leaf tissue will be sampled at multiple time points prior to and following symptom development. DNA will be extracted and tested using qPCR. A standard detection curve will be created by correlating Ct values with known conidial concentrations. The goal is to finalize a reliable and reproducible qPCR protocol capable of detecting the pathogen prior to visible symptom development, allowing for early intervention and management.

Objective 2: Evaluate the optimized qPCR assay under field and nursery conditions
To test the assay under real-world conditions, the validated protocol will be applied to strawberry leaves collected from a previously inoculated field experiment where disease development, cultivar response, and inoculum levels are documented. Sampling will be conducted at regular intervals, and the qPCR results will be compared to symptom development and field metadata. In addition, asymptomatic nursery plants will be sampled and tested using pooled leaf samples to evaluate batch testing feasibility. Metadata such as cultivar identity, source nursery, and management practices will be recorded. This objective will help assess the assay's detection sensitivity across a range of infection scenarios and confirm its value as a diagnostic tool for early disease detection in commercial settings.

Objective 3: Define the sensitivity limits of qPCR for detecting latent Neopestalotiopsis infections in pooled nursery samples

This objective aims to evaluate the minimum infection level detectable in mixed plant tissue samples. Artificially constructed pools of healthy and infected leaves at various ratios (e.g., 1:4, 1:9, 1:19) will be tested to determine the assay's sensitivity limits. These results will inform the development of sampling recommendations, including the number of plants per batch and pooling strategies appropriate for nursery surveillance. The goal is to create a practical, scalable batch-testing protocol that growers and diagnostic labs can implement to improve nursery clean plant certification efforts.

The outcome of this objective will enable nurseries to apply the assay confidently for routine testing, ensuring that even low-level infections can be detected early enough to prevent field outbreaks. It will provide the empirical foundation for nursery managers to make science-based decisions about testing frequency, batch size, and risk management.