Rhizosphere Microbiome Engineering for Climate-Smart Agriculture: From Synthetic Consortia to Precision Decision Support
5 affiliations
- 1International Center of Agricultural Research in Dry Areas (ICARDA), Giza 11742, Egypt
- 2Department of Biotechnology, Faculty of Science, Cairo University, Giza 12613, Egypt
- 3Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC), Giza 12619, Egypt
- 4Department of Biological Sciences, College of Science, King Faisal University, Al Ahsa 31982, Saudi Arabia
- 5Zoology Department, Faculty of Science, Damanhour University, Damanhour 22511, Egypt
Abstract
Rhizosphere microbiome engineering is a promising approach that can enhance crop resilience and input use efficiency by redirecting plant-microbe-soil interactions toward predictable functions. Here, we review the mechanistic bases underlying rhizosphere assembly and stability, including root exudate-mediated selection, priority effects, keystone taxa, and metabolite-driven signaling, and connect these principles to proposed design rules for microbial inoculants. We present a generalizable Design-Build-Test-Learn (DBTL) framework for engineering synthetic microbial consortia, covering trait-to-module mapping (nutrient acquisition, phytohormone modulation, ACC deaminase activity, stress-protective metabolites, and biocontrol), compatibility screening, minimal yet robust community architectures, and iterative optimization driven by multi-omics and high-throughput phenotyping. Translation to field settings is framed as an engineering challenge defined by formulation and administration limitations, including carrier type, seed coating and encapsulation methods, shelf life, strain invasiveness, and permanence of colonization amid environmental diversity. We also summarize how integrative measurement pipelines (amplicon and shotgun sequencing, transcriptomics, metabolomics, and network or causal analyses) can advance microbiome studies from correlation to actionability. We describe how precision agriculture (sensors, remote sensing, and variable-rate inputs) and AI/ML (split-sample comparisons, transfer learning, and active learning) approaches can accelerate strain discovery, mixture optimization, and adaptive experimentation, driven by the need for stringent controls, metadata-rich reporting, and cross-site comparability. Use cases focus on stress conditions (drought, salinity, thermal extremes, and biotic stress) to demonstrate how microbial functions translate to agronomic outcomes and to highlight critical bottlenecks for reproducible, scalable microbiome products.
Related
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BibTeX
@article{fouad2026rhizosphere,
title = {{Rhizosphere Microbiome Engineering for Climate-Smart Agriculture: From Synthetic Consortia to Precision Decision Support}},
author = {Nourhan Fouad and Emad M. Elzayat and Dina Amr and Dina A. El-Khishin and Khaled Hashem Radwan and Alaa Youssef and Abeer A. Khalaf and Hoda A Ahmed and Eman H. Radwan and Sawsan Tawkaz and Michaël Baum},
journal = {Microorganisms},
year = {2026},
month = may,
doi = {10.3390/microorganisms14051138},
url = {https://doi.org/10.3390/microorganisms14051138},
note = {CC BY},
}
RIS
TY - JOUR
TI - Rhizosphere Microbiome Engineering for Climate-Smart Agriculture: From Synthetic Consortia to Precision Decision Support
AU - Nourhan Fouad
AU - Emad M. Elzayat
AU - Dina Amr
AU - Dina A. El-Khishin
AU - Khaled Hashem Radwan
AU - Alaa Youssef
AU - Abeer A. Khalaf
AU - Hoda A Ahmed
AU - Eman H. Radwan
AU - Sawsan Tawkaz
AU - Michaël Baum
PY - 2026
DA - 2026/05/17
JO - Microorganisms
DO - 10.3390/microorganisms14051138
UR - https://doi.org/10.3390/microorganisms14051138
AB - Rhizosphere microbiome engineering is a promising approach that can enhance crop resilience and input use efficiency by redirecting plant-microbe-soil interactions toward predictable functions. Here, we review the mechanistic bases underlying rhizosphere assembly and stability, including root exudate-mediated selection, priority effects, keystone taxa, and metabolite-driven signaling, and connect these principles to proposed design rules for microbial inoculants. We present a generalizable Design-Build-Test-Learn (DBTL) framework for engineering synthetic microbial consortia, covering trait-to-module mapping (nutrient acquisition, phytohormone modulation, ACC deaminase activity, stress-protective metabolites, and biocontrol), compatibility screening, minimal yet robust community architectures, and iterative optimization driven by multi-omics and high-throughput phenotyping. Translation to field settings is framed as an engineering challenge defined by formulation and administration limitations, including carrier type, seed coating and encapsulation methods, shelf life, strain invasiveness, and permanence of colonization amid environmental diversity. We also summarize how integrative measurement pipelines (amplicon and shotgun sequencing, transcriptomics, metabolomics, and network or causal analyses) can advance microbiome studies from correlation to actionability. We describe how precision agriculture (sensors, remote sensing, and variable-rate inputs) and AI/ML (split-sample comparisons, transfer learning, and active learning) approaches can accelerate strain discovery, mixture optimization, and adaptive experimentation, driven by the need for stringent controls, metadata-rich reporting, and cross-site comparability. Use cases focus on stress conditions (drought, salinity, thermal extremes, and biotic stress) to demonstrate how microbial functions translate to agronomic outcomes and to highlight critical bottlenecks for reproducible, scalable microbiome products.
SN - 2076-2607
ER -