Background: The Southern root-knot nematode (Meloidogyne incognita) poses a substantial threat to cotton (Gossypium hirsutum L.) by causing significant agricultural losses. Host plant resistance is the most plausible approach for minimizing these losses. QTL mapping and early transcriptomic studies have identified candidate genes within the QTL regions on chromosome 11 (qMi-C11) and chromosome 14 (qMi-C14). Although these QTL regions have been fine-mapped and candidate genes identified, expression profiling of Meloidogyne-Gossypium interactions across different stages of infection could further refine the list of candidate genes. This study advances our understanding of the molecular mechanisms underlying the resistance conferred by qMi-C11 and qMi-C14 against Southern root-knot nematode.
Results: Using time-course RNA-seq analyses across nematode developmental phases, we uncovered transcriptomic events-both genome-wide and within QTL intervals-underlying defense responses during compatible interactions (with Cocker 201, a susceptible line) and incompatible interactions (with M-120 RNR, a resistant line). Basal defense responses were observed in both compatible and incompatible interactions, with stronger expression in the incompatible interaction. Nematode-responsive genes associated with defense pathways showed distinct dynamics, characterized by repression during compatible interactions and early induction, greater diversity, and heightened upregulation during incompatible interactions. This study uncovers a broad repertoire of disease resistance and putative resistance genes, as well as pathogenesis-related genes, ligands, and receptors, that are differentially expressed in response to nematode parasitism. Mapping of these genes across the cotton genome identified promising candidates, including Gh_A11G3090 (PUB21) and Gh_A11G2836 (RPPL1) within the chromosome 11 QTL region, andGh_D02G0257 (RLP12) and Gh_D02G0259 (RLP12) within the chromosome 14 QTL region.
Conclusions: The findings of this study deepen our understanding of host-nematode interactions, identify candidate genes for downstream applications, and contribute to advancements in resistance breeding and sustainable nematode management strategies.
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http://dx.doi.org/10.1186/s12864-025-11339-w | DOI Listing |
Angew Chem Int Ed Engl
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Beijing University of Chemical Technology, State Key Laboratory of Chemical Resource Engineering, CHINA.
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Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the number of patients in expression datasets is usually limited, and the data from different datasets with different gene expressions cannot be easily combined.
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March 2025
Clemens Schöpf Institute, Department of Chemistry, Technical University of Darmstadt, Peter-Grünberg-Straße 4, 64287, Darmstadt, Germany. Electronic address:
Native MS (nMS) is a key structural biology technique that makes it possible to study intact proteins and their interactions. Unfortunately, non-volatile salts are incompatible with nMS, which demands a laborious desalting procedure. Non-denaturing size-exclusion chromatography (SEC) allows both rapid desalting and separation and has previously been explored for nMS automation.
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View Article and Find Full Text PDFPlant Physiol Biochem
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College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, 650201, China; The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, 650201, China. Electronic address:
The accumulation of flavonoids facilitates plant resistance to biotic stress. However, few studies have explored the functions of flavonoids during the interaction between wheat and Puccinia striiformis Westendorp f. sp.
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