Un método no destructivo para detectar la infección por Magnaporthe grisea en plantas de arroz en una etapa...

Rice yields are severely affected by blast disease caused by Magnaporthe grisea (MGR), an ascomycete fungus. Plants and pathogens often interact through reprogramming of phytohormone-mediated signalling pathways, which alters the pattern of volatile organic compounds (VOCs) produced. Many of these VOCs can be used to predict specific diseases and are unique to specific pathogen invasions. A high-throughput technique that can detect new pathogen incursions at an early asymptomatic stage can increase our readiness to take mitigation action. In this study, we sought to develop a disease detection method that relies on signature volatile organic compounds (S-VOCs) emissions to detect MGR infection in rice at its earliest and presymptomatic stage. As S-VOCs in rice-MRG interactions have not yet been identified, rice leaves were artificially inoculated and their volatile profiles monitored at three stages: healthy (mock inoculated), MGR challenged (asymptomatic), and MGR challenged (symptomatic). In headspace solid-phase microextraction (HS-SPME), VOCs are collected for analysis by GC-MS. Among the 34 annotated VOCs, two compounds (octadecanal and 1-nonanol) were found only in MGR-inoculated plants at the asymptomatic stage. In addition, compared with healthy control plants, MGR-inoculated plants produced more methyl-salicylate (MeSA) and reactive oxygen species (ROS), indicating that MeSA and ROS play a role in short- and long-range signalling. In the early stages of MGR infection, when symptoms are barely noticeable, octadecanal and 1-nonanol were both able to distinguish between healthy and MGR-infected headspaces. This study further substantiates the potential for non-invasive early disease detection using VOCs.