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The nucleocapsid N is one of four structural proteins of the coronaviruses. Its essential role in genome encapsidation makes it a critical therapeutic target for COVID-19 and related diseases. However, the inherent disorder of full-length N hampers its structural analysis. Here, we describe a stepwise method using viral-derived RNAs to stabilize SARS-CoV-2 N for EM analysis. We identify pieces of RNA from the SARS-CoV-2 genome that promote the formation of structurally homogeneous N dimers, intermediates of assembly, and filamentous capsid-like structures. Building on these results, we engineer a symmetric RNA to stabilize N protein dimers, the building block of high-order assemblies, for EM studies. We combine domain-specific monoclonal antibodies against N with chemical cross-linking mass spectrometry to validate the spatial arrangement of the N domains within the dimer. Additionally, our cryo-EM analysis reveals novel antigenic sites on the N protein. Our findings provide insights into N protein´s architectural and antigenic principles, which can guide design of pan-coronavirus therapeutics.
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http://dx.doi.org/10.1038/s41467-025-61861-4 | DOI Listing |
J Fish Biol
September 2025
Education and Conservation Department, SeaWorld, San Diego, California, USA.
Drones are becoming increasingly useful in their ability to observe wildlife. They have been especially useful in documenting marine animals such as sharks. Here we present novel aerial drone observations of a previously unknown dorsal-fin behaviour in white sharks (Carcharodon carcharias).
View Article and Find Full Text PDFGenome Biol
September 2025
Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 100101, Beijing, China.
Background: Centromeres are crucial for precise chromosome segregation and maintaining genome stability during cell division. However, their evolutionary dynamics, particularly in polyploid organisms with complex genomic architectures, remain largely enigmatic. Allopolyploid wheat, with its well-defined hierarchical ploidy series and recent polyploidization history, serves as an excellent model to explore centromere evolution.
View Article and Find Full Text PDFMikrochim Acta
September 2025
Hunan Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, Hengyang Medical School, University of South China, Hengyang, 421001, China.
An Ag-functionalized structural color hydrogel (Ag-SCH) sensor is constructed for colorimetric detection of glutathione (GSH). The hydrogel is prepared by using the coordination of Ag and 1-vinylimidazole (1-VI) as cross-linking network. GSH acts as a competitive ligand to break the coordination between Ag and 1-VI, leading to the expansion and structural color change of the hydrogel.
View Article and Find Full Text PDFJ Robot Surg
September 2025
Department of General Surgery, Giglio Hospital Foundation, Cefalu', Italy.
The adoption of robotic pancreatectomy has grown significantly in recent years, driven by its potential advantages in precision, minimally invasive access, and improved patient recovery. However, mastering these complex procedures requires overcoming a substantial learning curve, and the role of structured mentoring in facilitating this transition remains underexplored. This systematic review and meta-analysis aimed to comprehensively evaluate the number of cases required to achieve surgical proficiency, assess the impact of mentoring on skill acquisition, and analyze how outcomes evolve throughout the learning process.
View Article and Find Full Text PDFNat Microbiol
September 2025
Division of Computational Pathology, Brigham and Women's Hospital, Boston, MA, USA.
Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled.
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