Image-based assessment of plant disease progression identifies new genetic loci for resistance to Ralstonia solanacearum in tomato.

A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image-based, non-destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time-series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image-based phenotyping for single and multi-traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image-based, non-destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.

Uncovering the Infection Strategy of Phyllachora maydis during Maize Colonization: A Comprehensive Analysis.

Tar spot, a disease caused by the ascomycete fungal pathogen Phyllachora maydis, is considered one of the most significant yield-limiting diseases of maize (Zea mays L.) within the United States. P. maydis may also be found in association with other fungi, forming a disease complex which is thought to result in the characteristic fish eye lesions. Understanding how P. maydis colonizes maize leaf cells is essential for developing effective disease control strategies. Here, we used histological approaches to elucidate how P. maydis infects and multiplies within susceptible maize leaves. We collected tar spot-infected maize leaf samples from four different fields in northern Indiana at three different time points during the growing season. Samples were chemically fixed and paraffin-embedded for high-resolution light and scanning electron microscopy. We observed a consistent pattern of disease progression in independent leaf samples collected across different geographical regions. Each stroma contained a central pycnidium that produced asexual spores. Perithecia with sexual spores developed in the stomatal chambers adjacent to the pycnidium, and a cap of spores formed over the stroma. P. maydis reproductive structures formed around but not within the vasculature. We observed P. maydis associated with two additional fungi, one of which is likely a member of the Paraphaeosphaeria genus; the other is an unknown fungi. Our data provide fundamental insights into how this pathogen colonizes and spreads within maize leaves. This knowledge can inform new approaches to managing tar spot, which could help mitigate the significant economic losses caused by this disease.