New phenotyping platform identifies key drought-tolerance genes in soybean
image:
Schematic representation of the workflow from developing weight lysimeter to the extraction of transpiration features.
view moreCredit: The authors
Using the device, the team analyzed 224 soybean recombinant inbred lines and extracted five traits related to slow wilting, identifying stress recognition time point (SRTP) and decrease in transpiration under stress (DTrs) as the most informative indicators. The approach uncovered a novel major QTL, qDTrs_Gm04, linked to drought resilience and pinpointed GmWRKY58 as a promising candidate gene.
Slow wilting—the ability of plants to maintain turgor and rigidity during drought—is a critical trait for climate-resilient agriculture. However, traditional transpiration assays rely on labor-intensive visual scoring or expensive physiological instruments, limiting studies to small populations and generating low-resolution data. Slow wilting results from multiple interacting processes, including stomatal conductance, water-use efficiency, osmotic regulation molecules, and leaf morphology. Current phenotyping methods often capture only final symptoms rather than the underlying dynamics, which leads to low QTL detection power and missed genetic targets. These challenges highlight the need for an affordable, automated system capable of continuous time-series measurement across large breeding populations.
A study (DOI: 10.1016/j.plaphe.2025.100102) published in Plant Phenomics on 7 September 2025 by Jungmin Ha’s team, Seoul National University, offers a scalable tool for accelerating drought-tolerant breeding.
The researchers first developed an automated phenotyping platform using low-cost load-cell weighing devices (about $5 each) controlled by microcontrollers to record pot weight every hour and thus estimate transpiration rates. They validated the system on 60 devices in a greenhouse using calibration weights (0, 200, 500, 1000 g) over 48 h, and linear regression yielded a slope of 1.00 and R² = 0.997, confirming high accuracy and consistency. To link transpiration dynamics with slow wilting, they calculated a slow-wilting index from wilting score and leaf moisture content, ranked 224 recombinant inbred lines, and selected 30 slowest- and 30 fastest-wilting lines as divergent groups. Weight logs under drought were denoised using LOWESS regression, revealing a characteristic stair-step pattern of daytime weight loss and nighttime stability that shifted to a plateau when plants perceived stress. From these curves, they extracted five traits: baseline transpiration (Trb), stress recognition time point (SRTP), stress-induced transpiration (Trs), decrease under stress (DTrs), and cumulative transpiration to SRTP (CTaSRTP). Applying these methods, the slow-wilting group showed significantly longer SRTP (157.2 vs 125.0 h) and much lower DTrs (47% vs 72%), indicating delayed stress perception and stronger reduction of transpiration. Correlation analysis across all lines identified SRTP and DTrs as the best physiological indicators of slow wilting, a conclusion reinforced by PCA, where they were the major contributors to the first principal component separating divergent groups. Machine-learning models (Random Forest, XGBoost) further highlighted SRTP, Trs, and DTrs as key predictors of slow-wilting traits. QTL mapping using SRTP and DTrs re-detected the known slow-wilting locus qSW_Gm10 with much higher explained variance and uncovered a novel major QTL, qDTrs_Gm04, from which GmWRKY58 emerged as a strong drought-related candidate gene based on expression, promoter variation, and regulatory network analyses.
This low-cost platform enables continuous, non-destructive, high-density phenotyping, providing a practical solution for large-scale drought-tolerance breeding. By generating time-series physiological data, breeders can identify elite lines earlier, reduce field-testing cost, and accelerate introgression of valuable alleles. The discovery of qDTrs_Gm04 and candidate gene GmWRKY58 offers new molecular targets for improving stomatal control and water-use efficiency, with direct applications in soybean improvement and potential adaptability across crops such as maize, wheat, and pulses.
###
References
DOI
Original Source URl
https://doi.org/10.1016/j.plaphe.2025.100102
Funding information
This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. RS-2025-00853272)" Rural Development Administration, Republic of Korea. This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2025-25431972).
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.
Journal
Plant Phenomics
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Development of an automated phenotyping platform and identification of a novel QTL for drought tolerance in soybean
No comments:
Post a Comment