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Rice blast, caused by Magnaporthe oryzae, is a major threat to global rice production, necessitating the development of resistant cultivars through genetic improvement. Breakthroughs in rice genomics, including the complete genome sequencing of japonica and indica subspecies and the availability of various sequence-based molecular markers, have greatly advanced the genetic analysis of blast resistance. To date, approximately 122 blast-resistance genes have been identified, with 39 of these genes cloned and molecularly characterized. The application of these findings in marker-assisted selection (MAS) has significantly improved rice breeding, allowing for the efficient integration of multiple resistance genes into elite cultivars, enhancing both the durability and spectrum of resistance. Pangenomic studies, along with AI-driven tools like AlphaFold2, RoseTTAFold, and AlphaFold3, have further accelerated the identification and functional characterization of resistance genes, expediting the breeding process. Future rice blast disease management will depend on leveraging these advanced genomic and computational technologies. Emphasis should be placed on enhancing computational tools for the large-scale screening of resistance genes and utilizing gene editing technologies such as CRISPR-Cas9 for functional validation and targeted resistance enhancement and deployment. These approaches will be crucial for advancing rice blast resistance, ensuring food security, and promoting agricultural sustainability. 

Abstract One of the common mechanisms to trigger plant innate immunity is recognition of pathogen avirulence gene products directly by products of major resistance (R) genes in a gene for gene manner. In the USA, theRgenes,Pik-s, PiKh/m, andPi-ta, Pi-39(t), andPtrgenes have been effectively deployed to prevent the infections ofM. oryzaeraces, IB49, and IC17 for some time.Pi-9is only recently being deployed to provide overlapped and complimentary resistance toMagnaporthe oryzaeraces IB49, IC17 and IE1k in the USA. Pi-ta, Pi-39(t), Pi9 are major nuclear binding site-leucine rich (NLR) proteins, and Ptr is an atypical R protein with 4 armadillo repeats. AlphaFold is an artificial intelligence system that predicts a protein 3D structure from its amino acid sequence. Here we report genome sequence analyses of the effectors and avirulence (AVR) genes,AVR-PitaandAVR-Pik, andAVR-Pi9, in 3 differentialM. oryzaeraces. Using AlphaFold 2 and 3 we find strong evidence of direct interactions of products of resistance genesPi-taandPikwithM. oryzaeavirulence (AVR) genes,AVR-PitaandAVR-Pikrespectively. We also found that AVR-Pita interacts with Pi-39(t) and Ptr, and Pi9 interacts with both AVR-Pi9 and AVR-Pik. Validation of direct interactions of two pairs of R and AVR proteins supported a direct interaction mechanism of plant innate immunity. Detecting interaction of both Ptr and Pi39(t) with AVR-Pita, and Pi-9 with both AVR-Pi9 and AVR-Pik, revealed a new insight into recognition of pathogen signaling molecules by these host R genes in triggering plant innate immunity. 

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 .