Lamins form a proteinaceous meshwork as a major structural component of the nucleus. Lamins, along with their interactors, act as determinants for chromatin organization throughout the nucleus. The major dominant missense mutations responsible for autosomal dominant forms of muscular dystrophies reside in the Ig fold domain of lamin A. However, how lamin A contributes to the distribution of heterochromatin and balances euchromatin, and how it relocates epigenetic marks to shape chromatin states, remains poorly defined, making it difficult to draw conclusions about the prognosis of lamin A-mediated muscular dystrophies. In the first part of this report, we identified the in vitro organization of full-length lamin A proteins due to two well-documented Ig LMNA mutations, R453W and W514R. We further demonstrated that both lamin A/C mutant cells predominantly expressed nucleoplasmic aggregates. Labeling specific markers of epigenetics allowed correlation of lamin A mutations with epigenetic mechanisms. In addition to manipulating epigenetic mechanisms, our proteomic studies traced diverse expressions of transcription regulators, RNA synthesis and processing proteins, protein translation components, and posttranslational modifications. These data suggest severe perturbations in targeting other proteins to the nucleus.
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http://dx.doi.org/10.1042/BCJ20240474 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Laboratory of Precision Medicine and Biopharmaceuticals, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Recurrent missense mutations in the human epidermal growth factor receptor 2 (HER2) have been identified across various human cancers. Among these mutations, the active S310F mutation in the HER2 extracellular domain stands out as not only oncogenic but also confers resistance to pertuzumab, an antibody drug widely used in clinical cancer therapy, by impeding its binding. In this study, we have successfully employed computational-aided rational design to undertake directed evolution of pertuzumab, resulting in the creation of an evolved pertuzumab variant named Ptz-SA.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.
Purpose: This work tests the viability of semi-supervision for brain metastases segmentation.
Chemistry
January 2025
RIKEN: Rikagaku Kenkyujo, Cluster for Pioneering Research, Hirosawa 2-1, 351-0198, Wako, JAPAN.
Protein immobilization technology is important in medical and industrial applications. We previously reported all-in-one in vitro selection, wherein a collagen-binding vascular endothelial growth factor (CB-VEGF) was identified from a fusion library of random and VEGF sequences. However, its interaction chemistry is mainly limited to the interaction established by the 20 canonical amino acids.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine (LSHTM), Keppel Street, London, WC1E 7HT, UK.
During the COVID-19 pandemic, heterologous vaccination strategies were employed to alleviate the strain on vaccine supplies. The Thailand Ministry of Health adopted these strategies using vector, inactivated, and mRNA vaccines. However, this approach has introduced challenges for SARS-CoV-2 sero-epidemiology studies.
View Article and Find Full Text PDFJ Voice
January 2025
Department of Electrical-Electronics Engineering, Erciyes University, Kayseri, Turkey. Electronic address:
Early diagnosis and referral are crucial in the treatment of voice disorders. Contemporary investigations have indicated the efficacy of voice pathology detection systems in significantly contributing to the evaluation of voice disorders, facilitating early diagnosis of such pathologies. These systems leverage machine learning methodologies, widely applied across diverse domains, and exhibit particular potential in the realm of voice pathology classification.
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