Drought stress has detrimental effects on crop productivity worldwide. A strong root system is crucial for maintaining water and nutrients uptake under drought stress. Wild watermelons possess resilient roots with excellent drought adaptability. However, the genetic factors controlling this trait remain uninvestigated. In this study, we conducted a bulk segregant analysis (BSA) on an F population consisting of two watermelon genotypes, wild and domesticated, which differ in their lateral root development under drought conditions. We identified two quantitative trait loci ( and ) associated with the lateral root response to drought. Furthermore, we determined that a small region (0.93 Mb in ) is closely linked to drought adaptation through quantitative trait loci (QTL) validation and fine mapping. Transcriptome analysis of the parent roots under drought stress revealed unique effects on numerous genes in the sensitive genotype but not in the tolerant genotype. By integrating BSA, fine mapping, and the transcriptome, we identified six genes, namely L-Ascorbate Oxidase (AO), Cellulose Synthase-Interactive Protein 1 (CSI1), Late Embryogenesis Abundant Protein (LEA), Zinc-Finger Homeodomain Protein 2 (ZHD2), Pericycle Factor Type-A 5 (PFA5), and bZIP transcription factor 53-like (bZIP53-like), that might be involved in the drought adaptation. Our findings provide valuable QTLs and genes for marker-assisted selection in improving water-use efficiency and drought tolerance in watermelon. They also lay the groundwork for the genetic manipulation of drought-adapting genes in watermelon and other species.
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http://dx.doi.org/10.3390/ijms25010065 | DOI Listing |
Angew Chem Int Ed Engl
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East China University of Science and Technology, Insitute of Fine Chemicals, Meilong Road 130, Shanghai, China, 200237, Shanghai, CHINA.
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Key Laboratory of Soybean Biology of Ministry of Education China, Key Laboratory of Soybean Biology and Breeding (Genetics) of Ministry of Agriculture and Rural Affairs, Northeast Agricultural University, Harbin, China.
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Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA. Electronic address:
Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white matter and cerebrospinal fluid. The presence of free water partial volume effects leads to biases in estimating diffusion properties. Additionally, the existing mathematical FWE model is a two-compartment model, which can be well posed for multi-shell data.
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Irrigation is a cornerstone of global food security, enabling sustainable agricultural production and helping to ensure that food is available for people around the world, now and in the future. Mapping irrigated fields provides valuable information for sustainable water management, agricultural development, and environmental conservation efforts. However, the collection of high-quality training data, which is necessary for accurate irrigation mapping remains costly and labour-intensive.
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Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Department of Psychology, Northwestern University, Evanston, IL 60208, USA. Electronic address:
Brain networks serving higher cognitive functions are widely distributed across frontal and posterior association zones. Two exceptions have been the parietal memory network (PMN) and salience network (SAL), which are typically restricted to posterior (e.g.
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