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PLoS One. 2014 Dec 26;9(12):e116020. doi: 10.1371/journal.pone.0116020. eCollection 2014.

A model to explain plant growth promotion traits: a multivariate analysis of 2,211 bacterial isolates.

Author information

1
Departamento de Genética, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
2
UNIVATES, Lajeado, RS, Brazil.
3
Departamento de Genética, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; Empresa de Pesquisa e Extensão Agropecuária de Santa Catarina (EPAGRI), Lages, SC, Brazil.

Abstract

Plant growth-promoting bacteria can greatly assist sustainable farming by improving plant health and biomass while reducing fertilizer use. The plant-microorganism-environment interaction is an open and complex system, and despite the active research in the area, patterns in root ecology are elusive. Here, we simultaneously analyzed the plant growth-promoting bacteria datasets from seven independent studies that shared a methodology for bioprospection and phenotype screening. The soil richness of the isolate's origin was classified by a Principal Component Analysis. A Categorical Principal Component Analysis was used to classify the soil richness according to isolate's indolic compound production, siderophores production and phosphate solubilization abilities, and bacterial genera composition. Multiple patterns and relationships were found and verified with nonparametric hypothesis testing. Including niche colonization in the analysis, we proposed a model to explain the expression of bacterial plant growth-promoting traits according to the soil nutritional status. Our model shows that plants favor interaction with growth hormone producers under rich nutrient conditions but favor nutrient solubilizers under poor conditions. We also performed several comparisons among the different genera, highlighting interesting ecological interactions and limitations. Our model could be used to direct plant growth-promoting bacteria bioprospection and metagenomic sampling.

PMID:
25542031
PMCID:
PMC4277451
DOI:
10.1371/journal.pone.0116020
[Indexed for MEDLINE]
Free PMC Article

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