Potential of MALDI-TOF mass spectrometry as a rapid detection technique in plant pathology: identification of plant-associated microorganisms

摘要

Plant diseases caused by plant pathogens substantially reduce crop production every year, resulting in massive economic losses throughout the world. Accurate detection and identification of plant pathogens is fundamental to plant pathogen diagnostics and, thus, plant disease management. Diagnostics and disease-management strategies require techniques to enable simultaneous detection and quantification of a wide range of pathogenic and non-pathogenic microorganisms. Over the past decade, rapid development of matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) techniques for characterization of microorganisms has enabled substantially improved detection and identification of microorganisms. In the biological sciences, MALDI-TOF MS is used to analyze specific peptides or proteins directly desorbed from intact bacteria, fungal spores, nematodes, and other microorganisms. The ability to record biomarker ions, in a broad m/z range, which are unique to and representative of individual microorganisms, forms the basis of taxonomic identification of microorganisms by MALDI-TOF MS. Recent advances in mass spectrometry have initiated new research, i.e. analysis of more complex microbial communities. Such studies are just beginning but have great potential for elucidation not only of the interactions between microorganisms and their host plants but also those among different microbial taxa living in association with plants. There has been a recent effort by the mass spectrometry community to make data from large scale mass spectrometry experiments publicly available in the form of a centralized repository. Such a resource could enable the use of MALDI-TOF MS as a universal technique for detection of plant pathogens and non-pathogens. The effects of experimental conditions are sufficiently understood, reproducible spectra can be obtained from computational database search, and microorganisms can be rapidly characterized by genus, species, or strain.