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Updated: May 6, 2026

Ecosystem Fabrication EcoFAB Protocols for The Construction of Laboratory Ecosystems Designed to Study Plant-microbe Interactions
Published on: April 10, 2018
Often in silico, rarely in vivo: characterizing endemic plant-associated microbes for system-appropriate
Holly Hone1,2,3, Tongda Li1,3, Jatinder Kaur1,3
1Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia.
Abstract:
The potential of phosphate-solubilizing microbes (PSMs) to enhance plant phosphorus uptake and reduce fertilizer dependency remains underutilized. This is partially attributable to frequent biofertilizer-farming system misalignments that reduce efficacy, and an incomplete understanding of underlying mechanisms. This study explored the seed microbiomes of nine Australian lucerne cultivars to identify and characterize high-efficiency PSMs. From a library of 223 isolates, 94 (42%) exhibited phosphate solubilization activity on Pikovskaya agar, with 15 showing high efficiency (PSI > 1.5). Genomic analysis revealed that the "high-efficiency" phosphate-solubilizing microbes belonged to four genera (Curtobacterium, Pseudomonas, Paenibacillus, Pantoea), including novel strains and species. However, key canonical genes, such as pqq operon and gcd, did not reliably predict phenotype, highlighting the limitations of in silico predictions. Mutagenesis of the high-efficiency isolate Pantoea rara Lu_Sq_004 generated mutants with enhanced and null solubilization phenotypes, revealing the potential role of "auxiliary" genes in downstream function of solubilization pathways. Inoculation studies with lucerne seedlings demonstrated a significant increase in shoot length (p < 0.05) following treatment with the enhanced-solubilization mutant, indicating a promising plant growth-promotion effect. These findings highlight the potential of more personalized "system-appropriate" biofertilizers and underscore the importance of integrating genomic, phenotypic, and in planta analyses to validate function. Further research is required to investigate links between genomic markers and functional outcomes to optimize the development of sustainable agricultural inputs.
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