Poster # 111

Peter Oppenheimer ¹, Francesco Tini ² , Briana Whitaker ³, Imane Laraba ³, Rebecca Whetten ⁴, and Christina Cowger ^1,4
1. North Carolina State University, Department of Entomology and Plant Pathology, Raleigh, NC 27695-76162
2. University of Perugia, Department of Agricultural, Food and Environmental Sciences, 06121 Perugia, Italy
3. USDA-ARS National Center for Agricultural Utilization Research, Mycotoxin Prevention and Applied Microbiology Unit, Peoria, IL 61604
4. USDA Agricultural Research Service, Plant Science Research Unit, Raleigh, NC 27695
Corresponding Author: Christina Cowger, christina.cowger@usda.gov

Peter Oppenheimer

Fusarium is a fungal genus encompassing a variety of species that cause Fusarium head blight (FHB) in small grains.  Fusarium produces mycotoxins during infection that can be harmful to human and livestock health and vary in type and toxicity by fungal species. In US wheat, the majority of FHB infections are caused by Fusarium graminearum; however, recent surveys have shown other species can be more prevalent in certain fields and up to 5 different Fusarium species can contaminate an individual wheat spike. Exploring this fusarial diversity means confronting the limited applicability of traditional diagnostic assays that rely on deriving single-spore isolates and Sanger sequencing of the translation elongation factor 1-a gene (TEF1), since fusaria do not grow at the same rate in culture and Sanger sequencing provides only qualitative results (present/absent).  Recognizing this limitation, qPCR and metabarcoding assays were developed that are culture-independent and provide insight into the abundance of different species in a single sample.  However, these approaches either (1) are low throughput, (2) are limited to a small subset of Fusarium, (3) cannot identify novel Fusarium, or (4) are not completely quantitative.  To overcome these limitations, we have developed a synthetic spike-in metabarcoding method (SSIM) that can quantify all fusaria in a single assay and is high-throughput with species-level resolution.  We compared the accuracy and precision of this assay against a lower-throughput quantitative PCR metabarcoding method (qM) and found that the two methods had near-identical behavior for quantifying F. graminearum, F. acuminatum, and F. poae in the same sample (R² ~ 0.99, 0.99, and 0.93 respectively) across a wide range of Fusarium concentrations within samples.  Additionally, we determined how to address the bias in estimates of absolute abundance that can be created by common bioinformatic pipelines that use read-quality filtering. This research represents the first reported use of SSIM in plant disease diagnostics and an improved tool for investigating Fusarium epidemiology and control.