Abstract
The role of microbial volatile organic compounds (MVOCs) in promoting plant growth has received much attention. We isolated Paenibacillus peoriae from mangrove rhizosphere soil, which can produce VOCs to promote the growth of Arabidopsis thaliana seedlings, increase the aboveground biomass of A. thaliana, and increase the number of lateral roots of A. thaliana. The effects of different inoculation amounts and different media on the composition of MVOCs were studied by solid-phase microextraction/gas chromatography-mass spectrometry (SPME/GC-MS) and headspace sampler/GC-MS. We found that the growth medium influences the function and composition of MVOCs. To survey the growth-promoting functions, the transcriptome of the receptor A. thaliana was then determined. We also verified the inhibitory effect of the soluble compounds produced by P. peoriae on the growth of 10 pathogenic fungi. The ability of P. peoriae to produce volatile and soluble compounds to promote plant growth and disease resistance has shown great potential for application in the sustainability of agricultural production. IMPORTANCE Microbial volatile organic compounds (MVOCs) have great potential as "gas fertilizers" for agricultural applications, and it is a promising research direction for the utilization of microbial resources. This study is part of the field of interactions between microorganisms and plants. To study the function and application of microorganisms from the perspective of VOCs is helpful to break the bottleneck of traditional microbial application. At present, the study of MVOCs is lacking; there is a lack of functional strains, especially with plant-protective functions and nonpathogenic application value. The significance of this study is that it provides Paenibacillus peoriae, which produces VOCs with plant growth-promoting effects and broad-spectrum antifungal activity against plant-pathogenic fungi. Our study provides a more comprehensive, new VOC component analysis method and explains how MVOCs promote plant growth through transcriptome analysis. This will greatly increase our understanding of MVOC applications as a model for other MVOC research.