Geno-pheno characterization of crop rhizospheres: An integrated Raman spectroscopy and microbiome approach in conventional and organic agriculture
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Abstract
In this study, we examined phenotypic and compositional patterns in rhizosphere microbial communities across conventionally and organically managed farms to assess impacts on soil microbiomes. We employed newly developed single-cell Raman microspectroscopy (SCRS)-based community phenotypic profiling analysis with microbiome 16S rRNA gene amplicon sequencing to compare the soil microbial communities of alfalfa, carrot, corn, lettuce, potato, soybean, squash, tomato, triticale, wheat, oat, and pea grown under either conventional or organic agriculture across farms in New York State (USA). Distinct microbiome clustering patterns indicated that organic and conventional production methods imposed strong selective pressures, shaping microbial assemblages within each group more distinctly than site or plant species variations. Using SCRS-based microbial phenotyping, we identified distinct microbial adaptations in agricultural soils, with organic systems favoring lipid-accumulating phenotypes for energy storage and stress resilience in low-input environments, while higher nutrient availability in conventional systems promoted carbon-rich phenotypes, enhancing rapid carbon assimilation and biomass production. Through network analysis of ecological hub species, we identified Pseudomonas , a plant growth-promoting rhizobacteria (PGPR), along with several nitrogen-fixing prokaryotes as core members within conventional agricultural systems. In contrast, organically managed soils featured PGPR taxa from the Bacilli class and contained microorganisms carrying antibiotic resistance genes, potentially indicating the presence of antibiotic resistance genes within organic agricultural environments. Overall, we found that the novel inclusion of microbial phenotyping methods, such as SCRS, can describe unique linkages between microbiome structure and their physiology that are distinctive between conventional and organic agricultural systems.
Importance
Our study successfully integrated single-cell Raman microspectroscopy and amplicon sequencing, two established techniques for analyzing microbial communities and their functions, enabling a link between genotype and phenotype to better characterize ecosystem dynamics. While few studies have explored microbial phenotypes alongside community composition to infer agricultural management differences, our research offered key insights into functional relevance of microbial communities to agricultural practices, demonstrating how management strategies influenced microbial adaptation. These findings advance microbial ecology research, demonstrating how agricultural management strategies influence microbiome structure and function, reinforcing the importance of phenotypic profiling in sustainable agriculture.
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To717evaluate the relationship between taxonomic and phenotypic alpha diversity metrics, we performed718both linear and log-transformed linear regression analyses between ASV- and OPU-derived richness,719Shannon, and evenness values. Ordinary least squares (OLS) regression models were fitted for each720treatment using the statsmodels Python package (v0.14.1) (105). In the log-transformed models, both721the independent and dependent variables were transformed using the natural logarithm of one plus722the value (log1p) to accommodate zero values and improve numerical stability using Python Numpy723(v2.2.4) (106). For each model, the coefficient of determination (R²) and corresponding P value were724extracted to assess the strength and significance of the relationship.
It would be nice to have a more comprehensive analysis of the …
To717evaluate the relationship between taxonomic and phenotypic alpha diversity metrics, we performed718both linear and log-transformed linear regression analyses between ASV- and OPU-derived richness,719Shannon, and evenness values. Ordinary least squares (OLS) regression models were fitted for each720treatment using the statsmodels Python package (v0.14.1) (105). In the log-transformed models, both721the independent and dependent variables were transformed using the natural logarithm of one plus722the value (log1p) to accommodate zero values and improve numerical stability using Python Numpy723(v2.2.4) (106). For each model, the coefficient of determination (R²) and corresponding P value were724extracted to assess the strength and significance of the relationship.
It would be nice to have a more comprehensive analysis of the relationship between OPU and ASV since there may be many drivers of correlation between OPU and ASV, prevalence of species being one, but also, you might have differing environmental factors diving correlation in OPU that deviates from ASV. If you could examine the correlation between OPU sets or features and environmental factors (such as organic\non-organic, or plant type) after controlling for ASV it might more directly identify aspects of biology that are driven to be similar based on growth conditions and not different species presence.
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