An experimental approach to study multi-species diseases interactions in wheat using machine learning-aided image analysis
Fusarium Head Blight (FHB) is a major disease of cereal crops present everywhere where wheat is produced. A complex of several, distinctly different, species from the genus Fusarium are responsible for causing the disease, with important taxa being F. graminearum, F. culmorum, and F. avenaceum. FHB is an adult stage, late season disease affecting wheat at anthesis. However, recent discoveries are now challenging the basic assumption that anthers and spikes are the only ecological niches relevant for resistance to FHB. Most prominently, it has been established that pathogens typically occurring earlier in the season, such as Zymoseptoria tritici, induce strong systemic changes in the plant immune system, that expand way beyond their primary ecological niche. Furthermore, taxa implicated in FHB can be isolated from virtually all wheat tissues, including typical blotch symptoms on wheat leaves. Strikingly, such important biological observations about the complex lifestyle of FHB causing taxa are overlooked as potential targets in resistance breeding. In this work we present a simplified approach allowing the dissection of the intricate lifestyles of the FHB causing Fusarium taxa. In doing so, we provide an experimental proof-of-concept that systemic signals from Z. tritici can affect the basic response of wheat to F. graminearum. We further demonstrate that differences in Fusarium species composition affect the outcome of the interaction in a genotype-specific manner. We argue that efforts to study hidden layers of the biology of FHB associated taxa are crucial to developing new resistance breeding strategies, based on a better understanding of the genetic drivers of the disease.