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The SARS-CoV-2 nucleocapsid protein is indispensable for viral RNA genome processing. Although the N-terminal domain (NTD) is suggested to mediate specific RNA-interactions, high-resolution structures with viral RNA are still lacking. Available hybrid structures of the NTD with ssRNA and dsRNA provide valuable insights; however, the precise mechanism of complex formation remains elusive. Similarly, the molecular impact of nucleocapsid NTD mutations that have emerged since 2019 has not yet been fully explored. Using crystallography and solution NMR, we investigate how NTD mutations influence structural integrity and RNA-binding. We find that both features rely on a core network of residues conserved in Betacoronaviruses, crucial for protein stability and communication among flexible loop-regions that facilitate RNA-recognition. Our comprehensive structural analysis demonstrates that contacts within this network guide selective RNA-interactions. We propose that the core network renders the NTD evolutionarily robust in stability and plasticity for its versatile RNA processing roles.
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http://dx.doi.org/10.1038/s41467-024-55024-0 | DOI Listing |
Neurobiol Dis
September 2025
F.M. Kirby Neurobiology Department, Boston Children's Hospital, Boston, MA, USA; Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Human Neuron Core, Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Boston, MA, USA.
CDKL5 deficiency disorder (CDD) is a rare developmental and epileptic encephalopathy resulting from variants in cyclin-dependent kinase-like 5 (CDKL5) that lead to impaired kinase activity or loss of function. CDD is one of the most common genetic etiologies identified in epilepsy cohorts. To study how CDKL5 variants impact human neuronal activity, gene expression and morphology, CDD patient-derived induced pluripotent stem cells and their isogenic controls were differentiated into excitatory neurons using either an NGN2 induction protocol or a guided cortical organoid differentiation.
View Article and Find Full Text PDFNeurosci Biobehav Rev
September 2025
Department of Biotechnology, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, 603203, Chengalpattu District, Tamil Nadu, India. Electronic address:
Gut-mitochondria is an emerging paradigm in understanding the pathophysiology of complex neuropsychiatric disorders such as Schizophrenia (SCZ). This bidirectional communication network connects the gastrointestinal microbiota with mitochondrial function and brain health, offering novel insights into disease onset and progression. SCZ, characterized by hallucinations, delusions, cognitive impairments, and social withdrawal, has traditionally been attributed to genetic and neurochemical imbalances.
View Article and Find Full Text PDFJ Ethnopharmacol
September 2025
Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China. Electronic address:
Ethnopharmacological Relevance: Acute lung injury (ALI) is a severe health issue characterized by high morbidity and mortality, driven by excessive inflammatory responses. The traditional Chinese medicine Huashi Baidu Granules (HBG) demonstrated clinical efficacy in treating severe ALI, yet its mechanisms remain unclear.
Aim Of The Study: This research aimed to examine the efficacy and underlying mechanisms of HBG in a lipopolysaccharide (LPS)-induced ALI model, identify core herbal constituents, active compounds, and therapeutic targets, providing a foundation for optimizing HBG-based treatments.
Neural Netw
September 2025
Department of Computer Science and Engineering, Hanyang University ERICA, 15588, South Korea. Electronic address:
In 6G mobile communication systems, various AI-based network functions and applications have been standardized. Federated learning (FL) is adopted as the core learning architecture for 6G systems to avoid privacy leakage from mobile user data. However, in FL, users with non-independent and identically distributed (non-IID) datasets can deteriorate the performance of the global model because the convergence direction of the gradient for each dataset is different, thereby inducing a weight divergence problem.
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