Multi-Omics Dissection of Drought Stress Responses in Crops: From Molecular Regulatory Networks to Climate-Resilient Breeding Applications
4 affiliations
- 1Faculty of Engineering, Computing and Science, Western Sydney University, Penrith, NSW 2751, Australia
- 2Research School of Biology, Australian National University, Acton, ACT 2601, Australia
- 3Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
- 4Department of Plant Physiology and Metabolomics, Agricultural Institute, HUN-REN Centre for Agricultural Research, 2462 Martonvásár, Hungary
Abstract
Drought stress is the most pervasive abiotic constraint on global crop productivity, with projected intensification under climate change threatening the yields of staple crops including wheat, rice, maize, and legumes. Conventional breeding approaches have delivered limited gains against drought tolerance, constrained by the polygenic and multifactorial nature of stress adaptation, the complexity of genotype-by-environment interactions, and the inadequacy of field-based phenotyping under variable stress conditions. Omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, have substantially advanced the molecular dissection of drought tolerance by enabling high-resolution characterization of stress-responsive genes, regulatory networks, adaptive proteins, and metabolic reprogramming pathways. Specific traits targeted include root system architecture and depth, osmotic adjustment capacity through proline and glycine betaine accumulation, antioxidant defense mechanisms, ABA-mediated stomatal regulation, LEA protein accumulation, epigenetic stress memory, and yield stability under water deficit. This review systematically examines omics-based strategies for drought stress mitigation across major crops, highlighting individual omics contributions, multi-omics integration frameworks, computational tools including machine learning and AI-driven predictive modelling, and translational breeding applications. Case studies in wheat, rice, maize, and legumes illustrate how omics-driven approaches accelerate precision breeding for drought resilience through marker-assisted selection, genomic selection, and CRISPR-based gene editing. Challenges including data integration complexity, high implementation costs, limited cross-species transferability, and the need for field-scale validation of microbiome-based strategies are critically addressed. Future perspectives encompassing single-cell and spatial omics, AI-driven predictive breeding, digital agriculture integration, and international data governance frameworks are discussed. By aligning with climate-smart agriculture principles, multi-omics approaches provide a robust and transformative foundation for developing drought-resilient crop cultivars suitable for water-limited production systems worldwide.
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BibTeX
@article{ali2026multiomics,
title = {{Multi-Omics Dissection of Drought Stress Responses in Crops: From Molecular Regulatory Networks to Climate-Resilient Breeding Applications}},
author = {Baber Ali and Zeeshan Khan and Nijat Imin and Tibor Janda and Fatemeh Gholizadeh},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = jun,
doi = {10.3390/ijms27115008},
url = {https://doi.org/10.3390/ijms27115008},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-Omics Dissection of Drought Stress Responses in Crops: From Molecular Regulatory Networks to Climate-Resilient Breeding Applications
AU - Baber Ali
AU - Zeeshan Khan
AU - Nijat Imin
AU - Tibor Janda
AU - Fatemeh Gholizadeh
PY - 2026
DA - 2026/06/01
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27115008
UR - https://doi.org/10.3390/ijms27115008
AB - Drought stress is the most pervasive abiotic constraint on global crop productivity, with projected intensification under climate change threatening the yields of staple crops including wheat, rice, maize, and legumes. Conventional breeding approaches have delivered limited gains against drought tolerance, constrained by the polygenic and multifactorial nature of stress adaptation, the complexity of genotype-by-environment interactions, and the inadequacy of field-based phenotyping under variable stress conditions. Omics technologies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, have substantially advanced the molecular dissection of drought tolerance by enabling high-resolution characterization of stress-responsive genes, regulatory networks, adaptive proteins, and metabolic reprogramming pathways. Specific traits targeted include root system architecture and depth, osmotic adjustment capacity through proline and glycine betaine accumulation, antioxidant defense mechanisms, ABA-mediated stomatal regulation, LEA protein accumulation, epigenetic stress memory, and yield stability under water deficit. This review systematically examines omics-based strategies for drought stress mitigation across major crops, highlighting individual omics contributions, multi-omics integration frameworks, computational tools including machine learning and AI-driven predictive modelling, and translational breeding applications. Case studies in wheat, rice, maize, and legumes illustrate how omics-driven approaches accelerate precision breeding for drought resilience through marker-assisted selection, genomic selection, and CRISPR-based gene editing. Challenges including data integration complexity, high implementation costs, limited cross-species transferability, and the need for field-scale validation of microbiome-based strategies are critically addressed. Future perspectives encompassing single-cell and spatial omics, AI-driven predictive breeding, digital agriculture integration, and international data governance frameworks are discussed. By aligning with climate-smart agriculture principles, multi-omics approaches provide a robust and transformative foundation for developing drought-resilient crop cultivars suitable for water-limited production systems worldwide.
SN - 1422-0067
SN - 1661-6596
ER -