Decoding Plant Adaptation and Resilience Through Integrative Multi-Omics Approaches

Climate change, increasing environmental variability, and the growing demand for sustainable crop production are intensifying the need to understand the mechanisms that enable plants to adapt and maintain resilience under stress. Recent advances in multi-omics technologies—including transcriptomics, metabolomics, epigenomics, proteomics, long-read sequencing, and systems biology—are transforming our ability to dissect the molecular architecture underlying plant adaptation. Complementary advances in quantitative and population genetics—including genome-wide association studies (GWAS), quantitative trait locus (QTL) mapping, population genomics, genomic prediction, and pangenome analyses—are further accelerating the discovery of genetic variants and regulatory mechanisms that underpin adaptative traits Despite these remarkable advances, integrating genomic variation with downstream molecular phenotypes remains a major challenge for understanding how genome structure, allelic diversity, and regulatory networks collectively shape plant adaptation and resilience. Integrative approaches now provide unprecedented opportunities to uncover how genetic variation, transcriptional regulation, metabolism, signalling pathways, and environmental signals converge to shape plant phenotypes under dynamic conditions. Emerging evidence from model and crop species—including studies on water stress responses and light-induced metabolic regulation—highlights the power of combining complementary omics approaches to achieve a comprehensive understanding of plant resilience. However, the genetic and molecular frameworks governing these complex adaptive processes remain only partially understood.This Research Topic aims to decode the genetic and molecular architecture underlying plant adaptation by integrating genomic, transcriptomic, metabolomics, and systems-level information across model and crop species. We seek to uncover how genome structure, allelic variation, and regulatory networks orchestrate metabolic and transcriptional reprogramming in response to environmental and developmental cues. Ultimately, this integrated framework will advance our understanding of the biological mechanisms that drive plant robustness, developmental plasticity, and productivity under fluctuating conditions. By integrating genomic variation with transcriptomic, metabolomic, and other multi-omic datasets, researchers can establish direct links between genotype and phenotype, identify causal genes and regulatory variants associated with adaptive traits, reconstruct regulatory networks, and translate these discoveries into innovative strategies for crop improvement. Integrative analyses combining GWAS, QTL mapping, population genomics, genomic prediction, and systems biology are particularly well suited to accelerate the discovery of the genetic architecture underlying stress adaptation. Key questions include how genomic variation shapes transcriptional and metabolic plasticity, how multilayer regulatory networks coordinate adaptive responses across diverse environments, how genetic variants associated with adaptive traits can be identified through quantitative and population genetics approaches, and how these discoveries can be leveraged to accelerate the development of resilient, high-performing and nutritionally enhanced crop varieties for future agricultural demands.We welcome contributions spanning model systems and crop species that combine experimental, computational, and quantitative genetics approaches to decipher the genetic and molecular basis of plant adaptation. We particularly encourage studies integrating genomics, transcriptomics, metabolomics, systems biology, and predictive modeling to bridge genotype, molecular phenotype, and adaptive traits. Submissions may include, but are not limited to, the following themes:o Metabolic reprogramming and signalling under environmental and developmental stresso Mechanistic studies on secondary metabolism and regulatory pathways governing resilienceo Transcriptomic dynamics during plant development and their linkage to metabolomic plasticityo Network inference and systems-level analyses revealing the interaction between metabolomic and transcriptomic dynamicso Artificial intelligence and machine learning approaches in plant biology and omics integrationo Multi-omics and systems biology strategies for improving plant quality, resilience, and yield under climate stressWe also welcome methodological advances, computational frameworks, databases, and analytical pipelines that facilitate the integration and interpretation of multi-omics datasets for understanding plant adaptation. Topic editors: Bharathi Raja Ramadoss, DAMAR LIZBETH LOPEZ-ARREDONDO, Arvind H. Hirani Source: https://www.frontiersin.org/research-topics/83687/decoding-plant-adaptation-and-resilience-through-integrative-multi-omics-approaches