Abstract
BACKGROUND: Plant-microbe interactions in the rhizosphere are central to plant growth, nutrient acquisition, and stress resilience. Although multi-omics approaches enable comprehensive profiling of different biological layers, integrating these data to understand the mechanisms underlying plant-microbe symbiosis, particularly under drought stress, remains a challenge. RESULTS: Genomic, metabolomic, and microbiome data from 198 soybean accessions grown under both control and drought conditions were integrated to identify environment-specific predictive features of the plant phenotypes. We compared best linear unbiased prediction (BLUP), genome-wide association study (GWAS), and a nonlinear machine learning model to evaluate their ability to detect informative features. The machine learning models provided flexible variable selection and outperformed linear models in capturing nonlinear dependencies. Model interpretation using SHapley Additive exPlanations (SHAP) indicated that the isoflavone derivative, daidzin, and the drought-tolerant Candidatus Nitrosocosmicus, were major contributors to phenotypic variation, specifically under drought stress. SHAP-based interaction networks indicated cross-omics links, including connections between daidzin, gamma-aminobutyric acid (GABA), and Paenibacillus. CONCLUSION: The proposed interpretable machine learning approach for plant phenotype prediction identified multi-omics biomarkers and interactions, providing insights into plant adaptation to drought stress through environment-dependent rhizosphere networks and symbiotic associations.
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Claims 4
Claim · model-written summary
Machine learning models outperformed linear models in capturing nonlinear dependencies.
- Taxon
- soybean
Glycine maxe1 - Outcome
- capturing nonlinear dependenciese0
- Direction
- outperformede0
- Comparator
- linear modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The machine learning models provided flexible variable selection and outperformed linear models in capturing nonlinear dependencies”
t0“Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions”
e1“198 soybean accessions grown under both control and drought conditions”
Claim · model-written summary
Daidzin was a major contributor to soybean phenotypic variation under drought.
- Taxon
- soybeanGlycine maxe1
- Compound
- daidzine0
- Outcome
- phenotypic variatione0
- Qualifier
- under drought stresse0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the isoflavone derivative, daidzin, and the drought-tolerant Candidatus Nitrosocosmicus, were major contributors to phenotypic variation, specifically under drought stress”
t0“Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions”
e1“198 soybean accessions grown under both control and drought conditions”
Claim · model-written summary
Candidatus Nitrosocosmicus was a major contributor to soybean phenotypic variation under drought.
- Taxon
- soybeanGlycine maxe1
- Interactor
- Candidatus NitrosocosmicusCandidatus Nitrosocosmicuse0symbiont
- Outcome
- phenotypic variatione0
- Qualifier
- under drought stresse0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the drought-tolerant Candidatus Nitrosocosmicus, were major contributors to phenotypic variation, specifically under drought stress”
t0“Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions”
e1“198 soybean accessions grown under both control and drought conditions”
Claim · model-written summary
SHAP networks linked daidzin, GABA and Paenibacillus across omics layers.
- Taxon
- soybeanGlycine maxe1
- Interactor
- Paenibacillus
Paenibacilluse0symbiont - Compound
- daidzine0
- Outcome
- cross-omics linkse0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“SHAP-based interaction networks indicated cross-omics links, including connections between daidzin, gamma-aminobutyric acid (GABA), and Paenibacillus”
t0“Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions”
e1“198 soybean accessions grown under both control and drought conditions”