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Roots remain an underexplored frontier in plant genetics despite their well-known influence on plant development, agricultural performance and competition in the wild. Visualizing and measuring root structures and their growth is vastly more difficult than characterizing aboveground parts of the plant and is often simply avoided. The majority of research on maize root systems has focused on their anatomy, physiology, development and soil interaction, but much less is known about the genetics that control quantitative traits. In maize, seven root development genes have been cloned using mutagenesis, but no genes underlying the many root-related quantitative trait loci (QTLs) have been identified. In this review, we discuss whether the maize mutants known to control root development may also influence quantitative aspects of root architecture, including the extent to which they overlap with the most recent maize root trait QTLs. We highlight specific challenges and anticipate the impacts that emerging technologies, especially computational approaches, may have toward the identification of genes controlling root quantitative traits.
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http://dx.doi.org/10.1093/pcp/pcy141 | DOI Listing |
Plant Biotechnol J
September 2025
College of Agronomy, Key Laboratory of High-Efficiency Production of Wheat-Maize Double Cropping, Henan Agricultural University, Zhengzhou, China.
The magnetic field is a continuously present environmental factor. It has been found that many species, including plants, can sense and utilise it. However, the effects of the magnetic field on plants and its potential utilisation, especially in crops, have been little explored.
View Article and Find Full Text PDFPestic Biochem Physiol
November 2025
Department of Biology & CESAM-Centre for Environmental and Marine Studies, University of Aveiro, Campus Universitario de Santiago, 3810-193 Aveiro, Portugal.
Maize (Zea mays L.) is one of the world's most widely cultivated and economically important cereal crop, serving as a staple food and feed source in over 170 countries. However, its global productivity is threatened by late wilt disease (LWD), a disease caused by Magnaporthiopsis maydis, that spreads through soil and seeds and can cause severe yield losses.
View Article and Find Full Text PDFPlant Physiol Biochem
September 2025
Department of Environmental Health, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China; MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, 030001, Shanxi, China; Center for Ecological Public Health Security of Ye
Nanoplastics (NPs) have raised increasing attention due to their potential environmental risks to terrestrial vegetation and food security. However, for the plants with various photosynthetic pathways, the differences in their photosynthetic response and related mechanisms upon NPs exposure are still unclear. Here, the photosynthetic responses of typical soybean and corn plants under polystyrene NPs (PSNPs) exposure were systematically compared for the first time.
View Article and Find Full Text PDFInt J Biol Macromol
September 2025
School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China. Electronic address:
Polycyclic aromatic hydrocarbons (PAHs) pose a significant threat to ecosystem security and human health. Laccase, a copper-containing oxidase, can oxidize aromatic compounds, potentially enhancing soil organic contaminants degradation and reducing secondary pollution risks in phytoremediation. However, the combined effects of laccase addition and soil temperature on phytoremediation efficiency remain underexplored.
View Article and Find Full Text PDFPlant Genome
September 2025
International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Estado de Mexico, Mexico.
Genomic selection is an extension of marker-assisted selection by leveraging thousands of molecular markers distributed across the genome to capture the maximum possible proportion of the genetic variance underlying complex traits. In this study, genomic prediction models were developed by integrating phenological, physiological, and high-throughput phenotyping traits to predict grain yield in bread wheat (Triticum aestivum L.) under three environmental conditions: irrigation, drought stress, and terminal heat stress.
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