ArticleNature microbiology2024
Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model.
Article in Nature microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging genome-scale metabolic models to understand aerobic methanotrophs.The ISME journal · 2024Pooled it
- Genome-based predictions of metabolic preferences and substrate phenotypes in psychrotrophic bacteria from permafrost environments.Microbiology spectrum · 2026Article
- Integrated Organic-Inorganic Fertilization Enhances Microbial Stoichiometric Homeostasis but Triggers Seasonal Metabolic Trade-Offs in an Alpine Sandy Ecosystem.Microorganisms · 2026Article
- Review
- Microbial growth rate is a stronger predictor of soil organic carbon than carbon use efficiency.Nature ecology & evolution · 2026Article
- Conservation agriculture buffers persistent carbon loss under warming by microbial adaptation.The ISME journal · 2026Article
- Genomic traits associated with copiotrophy decouple from maximum growth rate predictions along temperature gradients.The ISME journal · 2026Article
- Evolution of rhizobial siderophore utilization via accessory xeno-siderophore receptors and flexible intake machinery for self-produced siderophores.The ISME journal · 2026Article
- Carbon regime structures functional trait trajectories during primary succession in microorganisms.The ISME journal · 2026Article
- Systemic regulation of rhizosphere nutrient activation by polyol-chelated fertilizers: evidence and a conceptual framework.Frontiers in plant science · 2026Review
- Predicting rhizosphere-competence-related catabolic gene clusters in plant-associated bacteria with rhizoSMASH.Nature communications · 2025Article
- Regulation of the Microbiome in Soil Contaminated with Diesel Oil and Gasoline.International journal of molecular sciences · 2025Article
- Integrating Microbial Community Data Into an Ecosystem-Scale Model to Predict Litter Decomposition in the Face of Climate Change.Global change biology · 2025Article
- Rhizosphere-triggered viral lysogeny mediates microbial metabolic reprogramming to enhance arsenic oxidation.Nature communications · 2025Article
- Model behaviour.Nature microbiology · 2025Article
- Legacies of temperature fluctuations promote stability in marine biofilm communities.Nature communications · 2025Article
- A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry.Nature communications · 2025Article
- Growth of microbes in competitive lifestyles promotes increased ARGs in soil microbiota: insights based on genetic traits.Microbiome · 2025Article
- Humin oxidation drives microbial dehalogenation in oligotrophic environments.The ISME journal · 2025Article
- Bio-organic fertilizers reshape rhizosphere bacterial community and enhance crop productivity in reclaimed soil.Frontiers in microbiology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.