Septoria nodorum blotch (SNB), caused by Parastagonospora nodorum, is a severe foliar and glume disease on durum and common wheat. Pathogen-produced necrotrophic effectors (NEs) are the major determinants for SNB on leaves. One such NE is SnTox3, which evokes programmed cell death and leads to disease when recognized by the wheat Snn3-B1 gene. Here, we developed saturated genetic linkage maps of the Snn3-B1 region using two F2 populations derived from the SnTox3-sensitive line Sumai 3 crossed with different SnTox3-insensitive lines. Markers were identified and/or developed from various resources including previously mapped simple sequence repeats, bin-mapped expressed sequence tags, single nucleotide polymorphisms, and whole genome survey sequences. Subsequent high-resolution mapping of the Snn3-B1 locus in 5600 gametes delineated the gene to a 1.5 cM interval. Analysis of micro-colinearity of the Snn3-B1 region indicated that it was highly disrupted compared to rice and Brachypodium distachyon. The screening of a collection of durum and common wheat cultivars with tightly linked markers indicated they are not diagnostic for the presence of Snn3-B1, but can be useful for marker-assisted selection if the SnTox3 reactions of lines are first determined. Finally, we developed an ethyl methanesulfonate-induced mutant population of Sumai 3 where the screening of 408 M2 families led to the identification of 17 SnTox3-insensitive mutants. These mutants along with the markers and high-resolution map developed in this research provide a strong foundation for the map-based cloning of Snn3-B1, which will broaden our understanding of the wheat-P. nodorum system and plant-necrotrophic pathogen interactions in general.
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Sensors (Basel)
January 2025
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
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January 2025
Bureau of Emergency Management of Pingquan City, Pingquan 067500, China.
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View Article and Find Full Text PDFBiomolecules
January 2025
Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece.
Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial and biochemical features. This work summarizes the recent advances in computational approaches leveraging protein structure information for PPI prediction, focusing on machine learning (ML) and deep learning (DL) techniques.
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January 2025
Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China; University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address:
Despite recent advances in imaging- and antibody-based methods, achieving in-depth, high-resolution protein mapping across entire tissues remains a significant challenge in spatial proteomics. Here, we present parallel-flow projection and transfer learning across omics data (PLATO), an integrated framework combining microfluidics with deep learning to enable high-resolution mapping of thousands of proteins in whole tissue sections. We validated the PLATO framework by profiling the spatial proteome of the mouse cerebellum, identifying 2,564 protein groups in a single run.
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January 2025
NGO "Ukrainian Researchers Society", Ukraine; Institute of Geography of National Academy of Sciences of Ukraine, Ukraine.
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