Legume Genomics and Genetics 2026, Vol.17, No.1, 32-48 http://cropscipublisher.com/index.php/lgg 46 extensive drought-responsive splicing as key determinants of root architecture and water acquisition. Collectively, these achievements have created a rich catalog of candidate genes, pathways, and regulatory networks that now underpin molecular breeding strategies for drought-resilient soybean. Despite these advances, several conceptual and technical challenges limit the full translation of transcriptomic knowledge into robust drought-tolerant cultivars. Many studies rely on a small number of genotypes, often single cultivars or a few contrasting pairs, which constrains the ability to distinguish genotype-specific responses from broadly conserved tolerance mechanisms and complicates the extrapolation of findings to diverse germplasm. Experimental conditions frequently involve PEG-induced or acute drought treatments in controlled environments, whereas field drought is typically moderate, intermittent, and accompanied by other stresses; this discrepancy raises questions about how representative some expression signatures are of real-world condition. Moreover, most datasets capture static “snapshots” at a few time points, providing limited insight into the dynamics of regulatory cascades and the temporal coordination of stress perception, acclimation, and recovery. Another major challenge is the gap between transcript-level patterns and functional validation or breeding deployment. Only a small fraction of the many candidate DEGs and hub genes identified through GWAS-transcriptome integration, co-expression networks, or multi-omics analysis have been functionally characterized in soybean or heterologous systems . Yield-relevant phenotypes under multi-environment field trials remain even more rarely assessed, particularly for combinations of drought with heat, flooding, or biotic stresses. Additionally, drought tolerance is strongly stage- and tissue-dependent, yet many studies focus on a single stage, tissue, or stress level, making it difficult to build unified models that connect early-stage responses with reproductive performance and final yield. Finally, integrating large, heterogeneous datasets (different platforms, annotations, and analysis pipelines) into coherent, breeder-oriented resources is still technically challenging, and the development of user-friendly tools that link omics findings with markers, QTLs, and genomic selection pipelines is in its infancy. Future transcriptomic research on soybean drought stress will benefit from more integrative, field-relevant, and functionally anchored approaches. Large, diverse panels of cultivated and wild accessions evaluated under realistic, multi-environment drought scenarios should be combined with time-series RNA-seq in key tissues (roots, leaves, reproductive organs) to capture both the breadth and dynamics of drought responses. Coupling GWAS or RTM-GWAS with tissue-specific transcriptomics and co-expression/network analysis can refine drought-related QTLs and prioritize causal genes for breeding and editing, building on current successes in identifying candidate genes within major QTLs at germination and seedling stages. There is also a need to systematically validate transcription factor hubs (e.g., NAC, ERF, NF-Y, WRKY) and structural genes in ABA, phenylpropanoid, flavonoid, and ROS pathways using overexpression, CRISPR/Cas-mediated editing, and allele-swapping in elite backgrounds, with emphasis on whole-plant performance and yield stability under field drought. At the same time, multi-omics and post-transcriptional regulation should be more deeply integrated into drought research pipelines. Transcriptome-metabolome and transcriptome-proteome studies have already highlighted central roles for the TCA cycle, isoflavonoid and flavonoid biosynthesis, and tyrosine and linoleic acid metabolism in drought responses, while revealing co-expressed transcript-protein modules that likely represent robust tolerance nodes. Expanding these efforts across developmental stages and stress combinations (drought with heat, flooding, or pathogens) will clarify which pathways can be simultaneously optimized for multi-stress resilience. Genome-wide analyses of alternative splicing and splicing factor regulation indicate that isoform-level control is an important but underexploited layer of drought adaptation and should be incorporated into candidate-gene selection and functional assays. Ultimately, integrating transcriptomic, genomic, and multi-omics data into machine-learning-based genomic selection models, and embedding these models in breeder-friendly platforms linked to markers and decision tools, will be essential to convert molecular insights into climate-resilient soybean cultivars at scale.
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