Synthetic spike-in metabarcoding for plant pathogen diagnostics results in precise quantification of copy number within the genus Fusarium

Authors: OPPENHEIMER, PETER - North Carolina State University; TINI, FRANCESCO - University Of Perugia; Whetten, Rebecca; LARABA, IMANE - Agriculture And Agri-Food Canada; Read, Quentin; Whitaker, Briana; Vaughan, Martha; BECCARI, GIOVANNI - University Of Perugia; COVARELLI, LORENZO - University Of Perugia; Cowger, Christina

Submitted to: ISME Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/18/2025
Publication Date: 7/20/2025
Citation: Oppenheimer, P., Tini, F., Whetten, R.B., Laraba, I., Read, Q.D., Whitaker, B.K., Vaughan, M.M., Beccari, G., Covarelli, L., Cowger, C. 2025. Synthetic spike-in metabarcoding for plant pathogen diagnostics results in precise quantification of copy number within the genus Fusarium. ISME Communications. https://doi.org/10.1093/ismeco/ycaf124.
DOI: https://doi.org/10.1093/ismeco/ycaf124

Interpretive Summary: A synthetic spike-in metabarcoding (SSIM) assay is a way of identifying different fungal species in a single sample and comparing actual amounts of each species between samples. Until now, such assays have not been very robust and have not been widely adopted. Other researchers have tried to develop SSIM assays based on sequencing the ITS and 16S rRNA genes. This study uses the single-copy TEF1 gene, which has relatively uniform G+C content and length. This marks the first use of SSIM as a diagnostic test to identify and quantify plant pathogens within the genus Fusarium. Variability between species in the quality of sequencing reads was found to be a key source of bias, but it affects SSIM less than other similar approaches. SSIM was compared to another assay that used quantitative PCR to calculate the total number of DNA copies. The comparison showed that SSIM was both precise (R2 > 0.93 for three Fusarium species) and proportional (slope ~1) in relation to the quantitative PCR (qPCR) assay. To further validate SSIM, we applied it to 24 wheat grain samples from Italy. We detected a wide array of Fusarium species and associated mycotoxins, and SSIM was more accurate than qPCR in predicting toxin concentrations associated with most Fusarium species. Our results show how useful SSIM is in situations where pathogen species and their absolute abundances are unknown. This is useful for food safety and management of mycotoxin contamination.

Technical Abstract: Synthetic spike-in metabarcoding (SSIM) assays generate quantitative next-generation sequencing (NGS) data, but are marred by inconsistency and have seen limited adoption. Previous efforts to develop synthetic spike-in metabarcoding (SSIM) assays have focused on the ITS and 16S rRNA genes. This study marks the first use of SSIM as a diagnostic assay to identify and quantify plant pathogens within the genus Fusarium and implements it using the single-copy TEF1 gene, which has relatively uniform G+C content and length. We identified variability between species in read quality score as a key source of bias that impacts SSIM to a lesser extent than other quantitative NGS approaches. SSIM was validated against another quantitative NGS assay that utilized qPCR (qMET) to calculate the total copy number. The comparison showed that SSIM was both precise (R2 > 0.93 for three Fusarium species) and proportional (slope ~1) in relation to qMET. Further, we applied SSIM to 24 wheat grain samples from Italy, uncovering a diverse array of Fusarium species and associated mycotoxins, with SSIM demonstrating superior predictive accuracy for most toxin concentrations compared to qPCR. Our results underscore the utility of SSIM for pathogen-agnostic diagnostics, offering important implications for food safety and management of mycotoxin contamination.