Wheat Ear Microbiome State as a Transferable Indicator of Within-Season Mycotoxin Risk
5 affiliations
- 1Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW UK
- 2Business Unit Biointeractions and Plant Health, Plant Sciences Group, Wageningen University & Research, Droevendaalsesteeg 1, Wageningen, 6708PB The Netherlands
- 3Wageningen Food Safety Research (WFSR), Wageningen University & Research, Akkermaalsbos 2, Wageningen, 6708 WB Netherlands
- 4Department of Applied Chemistry, University of the Basque Country (UPV/EHU), 20018, San Sebastian, Spain
- 5Agronomy and Plant Science Branch, Agri-Food and Biosciences Institute (AFBI), Belfast, Northern Ireland UK
Abstract
Weather based models are central to mycotoxin risk assessment, but field level discrimination can be limited when environmental training data are sparse and models must transfer to locations not used during training. We tested whether wheat ear microbiome state provides a transferable biological indicator for within season classification of trichothecene contamination under strict spatial holdout, with relevance for food and feed safety. Across three winter wheat trial locations within a single production year, ears were sampled at BBCH 77 to 83, corresponding to mid to late grain filling, and paired bacterial 16 S rRNA gene and fungal ITS profiles were analysed alongside LC-MS/MS measurements of DON and T-2 + HT-2. Under leave one location out evaluation, microbiome informed models achieved substantially higher discrimination of higher versus lower contamination than station level weather summaries under identical spatial holdout. Weather features added little in the single season dataset, but simulation analyses indicated that environmental predictors become more learnable with temporal replication, supporting a future two stage framework in which early season weather context is refined by within season microbiome state. Portability screening and drop one ablation prioritised microbial indicators with sign consistent associations across locations and non-redundant predictive value. Together, these findings provide proof of concept that wheat ear microbiome state can support spatially transferable within season mycotoxin risk classification and targeted surveillance when environmental training data are limited.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s00248-026-02837-9.
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BibTeX
@article{kolawole2026wheat,
title = {{Wheat Ear Microbiome State as a Transferable Indicator of Within-Season Mycotoxin Risk}},
author = {Oluwatobi Kolawole and Anne D. van Diepeningen and Josipa Grzetic Martens and Ane Arrizabalaga-Larrañaga and Lisa Black and Leo van Overbeek},
journal = {Microbial Ecology},
year = {2026},
month = jul,
doi = {10.1007/s00248-026-02837-9},
url = {https://doi.org/10.1007/s00248-026-02837-9},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Wheat Ear Microbiome State as a Transferable Indicator of Within-Season Mycotoxin Risk
AU - Oluwatobi Kolawole
AU - Anne D. van Diepeningen
AU - Josipa Grzetic Martens
AU - Ane Arrizabalaga-Larrañaga
AU - Lisa Black
AU - Leo van Overbeek
PY - 2026
DA - 2026/07/24
JO - Microbial Ecology
DO - 10.1007/s00248-026-02837-9
UR - https://doi.org/10.1007/s00248-026-02837-9
AB - Weather based models are central to mycotoxin risk assessment, but field level discrimination can be limited when environmental training data are sparse and models must transfer to locations not used during training. We tested whether wheat ear microbiome state provides a transferable biological indicator for within season classification of trichothecene contamination under strict spatial holdout, with relevance for food and feed safety. Across three winter wheat trial locations within a single production year, ears were sampled at BBCH 77 to 83, corresponding to mid to late grain filling, and paired bacterial 16 S rRNA gene and fungal ITS profiles were analysed alongside LC-MS/MS measurements of DON and T-2 + HT-2. Under leave one location out evaluation, microbiome informed models achieved substantially higher discrimination of higher versus lower contamination than station level weather summaries under identical spatial holdout. Weather features added little in the single season dataset, but simulation analyses indicated that environmental predictors become more learnable with temporal replication, supporting a future two stage framework in which early season weather context is refined by within season microbiome state. Portability screening and drop one ablation prioritised microbial indicators with sign consistent associations across locations and non-redundant predictive value. Together, these findings provide proof of concept that wheat ear microbiome state can support spatially transferable within season mycotoxin risk classification and targeted surveillance when environmental training data are limited. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s00248-026-02837-9.
SN - 0095-3628
SN - 1432-184X
ER -