Search NASA⌕ Search

DOE OSTI · 3024993

A tissue‐resolved, network‐based transcriptomic framework for abiotic stress responses in sorghum

Abstract

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ko, Dae Kwan [Michigan State Univ., East Lansing, MI (United States); Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)] (ORCID:0000000297205138), Brandizzi, Federica [Michigan State Univ., East Lansing, MI (United States); Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)] (ORCID:0000000305808888). 2026-03-29. A tissue‐resolved, network‐based transcriptomic framework for abiotic stress responses in sorghum. https://doi.org/10.1111/tpj.70834

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops

Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment. This article is part of the theme issue ‘Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the ‘Resilience Revolution’?’.

abiotic stress↗