Abstract
Plant growth-promoting rhizobacteria (PGPR) engage in complex chemical exchange and signalling processes to enhance their survival, rhizosphere colonisation, and plant-beneficial roles. These microbial interactions are mediated by various chemical cues, including quorum sensing (QS) molecules, cyclic peptides, lipopeptides, nutrients, volatile organic compounds (VOC), and phytohormones. Cross-feeding, where one microorganism consumes metabolites produced by another, exemplifies direct chemical communication that shapes community dynamics and metabolic cooperation. However, the effects of cross-feeding among different PGPR strains remain insufficiently characterised. In this study, an LC–MS-based metabolomics approach, combined with multivariate statistical analysis, was employed to investigate metabolic perturbations induced by cross-feeding among PGPR strains. Growth curve analysis revealed that cross-fed PGPR exhibited growth patterns comparable to controls, with a slight reduction in biomass. Metabolic profiling indicated time-dependent shifts in the metabolic state of the cross-fed organisms, suggesting adaptive metabolic reprogramming in response to the donor-conditioned media. Multivariate analysis identified distinct metabolite alterations between cross-fed and control groups across different time points, highlighting the influence of nutrient availability on microbial growth dynamics. Notably, cross-fed groups showed decreased levels of primary metabolites such as amino acids and sugars alongside increased production of secondary metabolites, including surfactins, salicylic acid, and carboxylic acids. These secondary metabolites are implicated in plant growth promotion and defence, indicating their potential as natural biostimulants. The findings advance the understanding of PGPR interactions and chemical communication in the rhizosphere, supporting the development of sustainable agricultural practices by leveraging beneficial microbial interactions. Future research should explore these interactions within more complex microbial communities.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 University of Johannesburg, Imbewu Metabolomics Research Group, Department of Biochemistry, Faculty of Science, Auckland Park, South Africa (GRID:grid.412988.e) (ISNI:0000 0001 0109 131X); University of Johannesburg, Research Centre for Plant Metabolomics, Faculty of Science, Auckland Park, South Africa (GRID:grid.412988.e) (ISNI:0000 0001 0109 131X)
2 University of Johannesburg, Ubuntu Lab, Department of Biochemistry, Faculty of Science, Auckland Park, South Africa (GRID:grid.412988.e) (ISNI:0000 0001 0109 131X)
3 Estación Experimental del Zaidín, CSIC, Department of Stress, Development and Signalling in Plants, Granada, Spain (GRID:grid.418877.5) (ISNI:0000 0000 9313 223X)
4 University of Venda, Department of Biochemistry and Microbiology, Faculty of Science, Engineering and Agriculture, Thohoyandou, South Africa (GRID:grid.412964.c) (ISNI:0000 0004 0610 3705)





