A tissue-resolved, network-based transcriptomic framework for abiotic stress responses in sorghum
D.K. Ko and F. Brandizzi "A tissue-resolved, network-based transcriptomic framework for abiotic stress responses in sorghum" The Plant Journal (2026) 126:e70834 [DOI: 10.1111/tpj.70834]
Developing climate-resilient crops requires a detailed understanding of stress-induced gene expression dynamics, as maladaptive responses can compromise their productivity and survival. Sorghum, a globally important cereal with exceptional tolerance to multiple abiotic stresses, provides a powerful system for investigating these dynamics. However, how stress type, tissue specificity, and temporal progression jointly shape transcriptomic responses in crops remains poorly understood. Here, we present a comparative, time-resolved transcriptomic atlas of sorghum responses to drought, heat, and salinity stress across shoot and root tissues. Integrative analyses revealed that tissue specificity is the dominant determinant of abiotic stress-induced gene reprogramming across all three stresses. Building on these global comparisons, we focused on heat stress, as it elicited the most coherent and pronounced transcriptional and regulatory responses, enabling deeper network-level interrogation. Co-expression network analysis identified tissue-specific modules enriched for phytohormone-responsive genes, while gene regulatory network (GRN) mapping and cistrome analyses uncovered transcription factors (TFs) controlling key hub genes within these modules. Together, this study provides a foundational transcriptomic and network-based resource for dissecting the regulatory architecture of abiotic stress responses in sorghum and offers prioritized candidates for future functional validation and engineering of climate-resilient crops.
All data supporting the findings of this study are available within this paper and its Supplementary Materials files. The raw data of RNA-seq have been deposited to the National Center for Biotechnological Information Sequence Read Archive and are accessible via BioProject accession codes PRJNA1188145 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1188145). The processed data of RNA-seq analyses are available in Supplementary Data. Code availability: The scripts used in this study are available in GitHub (https://github.com/DaeKwan-Ko/abiotic_bulkrnaseq).