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Phyllachora maydis is a fungal plant pathogen that causes tar spot of corn ( Zea mays) in North and South America, causing devastating yield losses under favorable conditions. Although the causal agent is relatively easy to diagnose via macroscopic and microscopic observations, other diseases and conditions, such as insect frass, have been mistaken for tar spot of corn. Furthermore, conidia and ascospores in isolation can be difficult to visually distinguish from other fungi, and the development of signs and symptoms of the disease may not be observed until 12 to 20 days after infection. Therefore, we developed a TaqMan quantitative polymerase chain reaction (qPCR) assay for the detection and quantification of this pathogen to be used for diagnostics and airborne spore quantification. The assay was designed for the internal transcribed spacer region of P. maydis. The specificity of the assay was confirmed and tested against various nontarget Phyllachora species, corn pathogens, endophytes, and P. maydis samples from several states in the Midwest and from Mexico. The detection limit of this assay was determined to be 100 fg of genomic P. maydis DNA. To demonstrate the transferability of this technology, the assay was tested in different labs using various qPCR thermal cyclers. This assay can be used in downstream research involving latency period, disease prediction, and diagnostics. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license . 

Tar spot is a devasting corn disease caused by the obligate fungal pathogen Phyllachora maydis. Since its initial identification in the United States in 2015, P. maydis has become an increasing threat to corn production. Despite this, P. maydis has remained largely understudied at the molecular level due to difficulties surrounding its obligate lifestyle. Here, we generated a significantly improved P. maydis nuclear and mitochondrial genome using a combination of long- and short-read technologies and also provide the first transcriptomic analysis of primary tar spot lesions. Our results show that P. maydis is deficient in inorganic nitrogen utilization, is likely heterothallic, and encodes for significantly more protein coding genes, including secreted enzymes and effectors, than previous determined. Furthermore, our expression analysis suggests that following primary tar spot lesion formation, P. maydis might reroute carbon flux away from DNA replication and cell division pathways and towards pathways previously implicated in having significant roles in pathogenicity, such as autophagy and secretion. Together, our results identified several highly expressed unique secreted factors that likely contribute to host recognition and subsequent infection, greatly increasing our knowledge of the biological capacity of P. maydis, which have much broader implications for mitigating tar spot of corn. 

Since they emerged approximately 125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests that there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.