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Along with barley and rice, maize provides staple food for more than half of the world population. Maize ears are regularly infected with fungal pathogens of the Fusarium genus, which, besides reducing yield, also taint grains with toxic metabolites. In an earlier work, we have shown that maize ears infection with single Fusarium strains was detectable through volatile sensing. In nature, infection most commonly occurs with more than a single fungal strain; hence we tested how the interactions of two strains would modulate volatile emission from infected ears. For this purpose, ears of a hybrid and a dwarf maize variety were simultaneously infected with different strains of Fusarium graminearum and F. verticillioides and, the resulting volatile profiles were compared to the ones of ears infected with single strains. Disease severity, fungal biomass, and the concentration of the oxylipin 9-hydroxy octadecadienoic acid, a signaling molecule involved in plant defense, were monitored and correlated to volatile profiles. Our results demonstrate that in simultaneous infections of hybrid and dwarf maize, the most competitive fungal strains had the largest influence on the volatile profile of infected ears. In both concurrent and single inoculations, volatile profiles reflected disease severity. Additionally, the data further indicate that dwarf maize and hybrid maize might emit common (i.e., sesquiterpenoids) and specific markers upon fungal infection. Overall this suggests that volatile profiles might be a good proxy for disease severity regardless of the fungal competition taking place in maize ears. With the appropriate sensitivity and reliability, volatile sensing thus appears as a promising tool for detecting fungal infection of maize ears under field conditions.
The biotrophic pathogen Ustilago maydis causes smut disease on maize (Zea mays) and induces the formation of tumours on all aerial parts of the plant. Unlike in other biotrophic interactions, no gene-for-gene interactions have been identified in the maize–U. maydis pathosystem. Thus, maize resistance to U. maydis is considered a polygenic, quantitative trait. Here, we study the molecular mechanisms of quantitative disease resistance (QDR) in maize, and how U. maydis interferes with its components. Based on quantitative scoring of disease symptoms in 26 maize lines, we performed an RNA sequencing (RNA-Seq) analysis of six U. maydis-infected maize lines of highly distinct resistance levels. The different maize lines showed specific responses of diverse cellular processes to U. maydis infection. For U. maydis, our analysis identified 406 genes being differentially expressed between maize lines, of which 102 encode predicted effector proteins. Based on this analysis, we generated U. maydis CRISPR/Cas9 knock-out mutants for selected candidate effector sets. After infections of different maize lines with the fungal mutants, RNA-Seq analysis identified effectors with quantitative, maize line-specific virulence functions, and revealed auxin-related processes as a possible target for one of them. Thus, we show that both transcriptional activity and virulence function of fungal effector genes are modified according to the infected maize line, providing insights into the molecular mechanisms underlying QDR in the maize–U. maydis interaction.