Leaf spots caused by Curvularia lunata infection pose a significant threat to global maize production. Although resistance gene breeding faces challenges due to pathogen evolution, the plant microbiome has emerged as a key modulator of disease resistance. However, the mechanisms via which plant genes regulate phyllosphere metabolites to recruit beneficial microbes remain poorly understood. Here, we combined gene mapping, metabolomics, microbiome analyses, cytological analysis, and in vitro and in vivo experiments to investigate the disease resistance mechanism of ZmHPATR1. We first identified that the loss‐of‐function mutation in ZmHPATR1 significantly increased the levels of fumaric acid, folic acid, and tetrahydrofolic acid in the leaves, leading to the enrichment of the genus Sphingomonas. We further demonstrated that the extracellular polysaccharide, welan gum, biosynthesized by Sphingomonas, effectively inhibited C. lunata growth and disrupted its cell structure. These results enable us to comprehensively understand the complicated mechanisms of plant resistance to disease through a four‐level regulatory network that links plant genes, metabolites, microbes, and pathogens. Our findings provide new strategies for targeted microbiome‐based disease‐resistant breeding and the development of novel biopesticides for maize.
Xinhao Luo, Hanchen Shan, Boyan Wang et al.· New Phytologist· 0 citations
Antimicrobial peptides (AMPs) are primary candidates for addressing bacterial resistance. Although their target spectrum specificity varies significantly between Gram-positive and Gram-negative bacteria, current predictive models generally lack experimental validation. In this study, we constructed various machine learning models based on known sequences to systematically evaluate the performance of k-mer frequencies, physicochemical properties, and hybrid features in distinguishing the AMP target specificity. Results indicated that the random forest model based on eight key physicochemical properties performed best, achieving a test set accuracy of 82.09% with balanced classification and robust generalization. Feature importance analysis revealed that hydrophilicity and isoelectric point (pI) are the core physicochemical factors determining the target spectrum differences. The model was rigorously validated through a dual-track approach: first, via the synthesis and in vitro testing of 18 novel protozoan-derived AMPs (overall accuracy 66.67%) and, second, through a blind test on 55 independent external sequences, achieving a robust accuracy of 81.82%. Furthermore, the framework successfully identified candidates with potent activity against multidrug-resistant pathogens including Pseudomonas aeruginosa and Klebsiella pneumoniae. This experimentally validated predictive framework provides a reliable computational tool for the high-throughput screening and rational design of targeted antimicrobial peptides.
Peicheng Lu, Wenhao Li, Muhammad Zubair et al.· ACS Omega· 0 citations
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