Phytophthora phytopathogens from Oomycota cause devastating crop losses and threaten food security. However, Phytophthora secreted proteins that interact with plant-hosts remain underexplored. Here, auxiliary activity family 7 (AA7) enzymes from Ascomycota and Oomycota phytopathogens were shown to oxidise pectin-derived galacturonic acid and/or oligogalacturonides (OGs). Unique mono-cysteinyl-FAD oxidases with positively-charged active sites, suited to oxidise OGs, were discovered in Phytophthora sojae. The P. sojae OG oxidase genes, prevalent in this genus, were co-transcribed with pectin-degradation counterparts during early infection of soybean. Single OG oxidase knockouts significantly decreased P. sojae biomass in planta, potentially linking OG oxidases to virulence. We propose that oxidation by AA7 enzymes impairs the elicitor activity of OGs, potentially contributing to stealth Oomycota infection. Oxidation of OGs unravels a previously unknown microbial mechanism that contributes to evade plant immune-response against pathogens. Our findings highlight a unique oxidase architecture and hitherto unexplored targets for bioprotection from major plant pathogens.

The maize inbred line Chang7-2 and derived lines are important male donors for hybrid production, contributing significantly to the development of high-yie

Leafy (Lfy1) is a classical dominant mutant showing more leaf number above primary ear and later flowering time in maize, but the causal gene together with its underlying genetic mechanism are unknown. Here, we report the cloning of Lfy1 mutant, and find that a retrotransposon insertion leads to leafy phenotype by elevating expression of its neighboring gene ZmOM66. ZmOM66 encodes an AAA+ ATPase that locate in mitochondria and interacts with itself. ZmOM66 overexpression affects the starch degradation, as well as contents of glucose, pyruvic acid, trehalose-6-phosphate, and TCA cycle related amino acids, and influences expression patterns of circadian clock genes. Moreover, expressions of floral related genes, including photoperiod regulated gene ZmPHYB1, integrator genes ZCN7, ZNC8 and ZCN12, and floral meristem identity genes ZMM4, ZMM15, and MASD67, are also significantly decreased by ZmOM66 overexpression. These results deepen our understanding of the regulatory mechanism of floral transition and leaf number in plant.

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Here we employed an GWAS approach using k-mers, short substrings from sequencing reads. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and pathway genes directly found k-mers from causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, phenotypic prediction of kernel oil, leaf angle, and flowering time using k-mer data showed at least a similarly high prediction accuracy to the standard SNP-based method. Collectively, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

Artificial intelligence for science is a methodology that integrates artificial intelligence into scientific research to improve the precision and efficiency of data analysis and experimental processes. Specifically in potato late blight severity grading, due to the demand for both accuracy and cost-effective deployment, traditional methods are limited by subjective evaluation and timeconsuming manual measurement. In this paper, a lightweight grading model based on an enhanced YOLOv8-UNet3Plus network is proposed to enable objective and accurate potato late blight severity grading. In detail, the YOLOv8 network is optimized by integrating Spatial and Channel Reconstruction Convolution module, Bi-directional Feature Pyramid Network and Powerful-IoU loss, the UNet3Plus network is optimized by embedding Ghost convolution and Multi-Scale Local Response Attention. Experiments on real-world potato late blight datasets demonstrate that our model achieves an precision of 95.73% for leaf localization and an mean Intersection over Union of 82.65% for infected region segmentation with reduced parameters and computational cost. This AI4Science-based model provides an effective solution for potato late blight severity grading.