Profiling of Volatile Organic Compounds from Four Plant Growth-Promoting Rhizobacteria by SPME-GC-MS: A Metabolomics Study.
Msizi I MhlongoLizelle A PiaterIan A DuberyPublished in: Metabolites (2022)
The rhizosphere microbiome is a major determinant of plant health. Plant-beneficial or plant growth-promoting rhizobacteria (PGPR) influence plant growth, plant development and adaptive responses, such as induced resistance/priming. These new eco-friendly choices have highlighted volatile organic compounds (biogenic VOCs) as a potentially inexpensive, effective and efficient substitute for the use of agrochemicals. Secreted bacterial VOCs are low molecular weight lipophilic compounds with a low boiling point and high vapor pressures. As such, they can act as short- or long-distance signals in the rhizosphere, affecting competing microorganisms and impacting plant health. In this study, secreted VOCs from four PGPR strains ( Pseudomonas koreensis (N19) , Ps. fluorescens (N04) , Lysinibacillus sphaericus (T19) and Paenibacillus alvei (T22)) were profiled by solid-phase micro-extraction gas chromatography mass spectrometry (SPME-GC-MS) combined with a multivariate data analysis. Metabolomic profiling with chemometric analyses revealed novel data on the composition of the secreted VOC blends of the four PGPR strains. Of the 121 annotated metabolites, most are known as bioactives which are able to affect metabolism in plant hosts. These VOCs belong to the following classes: alcohols, aldehydes, ketones, alkanes, alkenes, acids, amines, salicylic acid derivatives, pyrazines, furans, sulfides and terpenoids. The results further demonstrated the presence of species-specific and strain-specific VOCs, characterized by either the absence or presence of specific VOCs in the different strains. These molecules could be further investigated as biomarkers for the classification of an organism as a PGPR and selection for agricultural use.
Keyphrases
- plant growth
- data analysis
- escherichia coli
- public health
- gas chromatography mass spectrometry
- healthcare
- single cell
- mental health
- risk assessment
- machine learning
- heavy metals
- human health
- mass spectrometry
- deep learning
- ms ms
- health information
- electronic health record
- high glucose
- pseudomonas aeruginosa
- artificial intelligence
- cystic fibrosis
- social media
- diabetic rats