Downregulation of E-cadherin is a hallmark of epithelial-mesenchymal transition (EMT), an essential component of cancer progression to more aggressive phenotypes characterized by tumor dedifferentiation, infiltration, and metastasis. However, the underlying mechanism for E-cadherin downregulation in hepatitis C virus (HCV)-associated hepatocellular carcinoma (HCC) is still unclear. In this study, we found that ectopic expression of HCV core protein or infection with HCV in human hepatocytes upregulated the levels of the transcriptional repressors, E12 and E47, resulting in inactivation of the E-cadherin promoter, containing E-box motifs, and subsequent repression of its expression. E12/E47 knock-down almost completely abolished the potential of HCV core protein to repress E-cadherin expression. HCV core protein inhibited ubiquitin-dependent proteasomal degradation of E12/E47 without affecting their expression at the transcriptional level. E12/E47 upregulation ultimately led to EMT in human hepatocytes, as demonstrated by morphological changes, altered expression levels of EMT markers, including E-cadherin, plakoglobin, and fibronectin, and increased capacity for cell detachment and migration. In conclusion, HCV core protein represses E-cadherin expression by upregulating E12/E47 levels to induce EMT in HCV-associated HCC.
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http://dx.doi.org/10.1016/j.canlet.2015.03.032 | DOI Listing |
Inflamm Res
January 2025
Department of Biochemistry, Cancer Biology, Neuroscience, and Pharmacology, School of Medicine, Meharry Medical College, 1005 D.B. Todd Jr. Blvd, Nashville, TN, USA.
Background: The aberrant expression of α defensin 5 (DEFA5) protein in colonic inflammatory bowel diseases (IBDs) underlies the distinct pathogenesis of Crohn's colitis (CC). It can serve as a biomarker for differentiating CC from Ulcerative colitis (UC), particularly in Indeterminate colitis (IC) cases into UC and CC. We evaluated the specificity of commercially available anti-DEFA5 antibodies, emphasizing the need to further validate their appropriateness for a given application and highlighting the necessity for novel antibodies.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Pharmaceutics, and Nanjing Medical University, Nanjing 211166, P. R. China.
Understanding the interaction between nanomaterials and cellular structures is crucial for nanoparticle applications in biomedicine. We have identified a subtype of stress granules, called nanomaterial-provoked stress granules (NSGs), induced by gold nanorods (AuNRs). These NSGs differ from traditional SGs in their physical properties and biological functions.
View Article and Find Full Text PDFFront Cell Infect Microbiol
January 2025
Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran.
Background: is a significant cause of healthcare-associated infections, with rising antimicrobial resistance complicating treatment. This study offers a genomic analysis of , focusing on sequence types (STs), global distribution, antibiotic resistance genes, and virulence factors in its chromosomal and plasmid DNA.
Methods: A total of 19,711 genomes were retrieved from GenBank.
Front Microbiol
January 2025
Department of Biomedical Sciences, Nazarbayev University School of Medicine, Astana, Kazakhstan.
Background: HCV genotypes are 30-35% polymorphic at the nucleotide level, while subtypes within the same genotype differ by nearly 20%. Although previous studies have shown the immune escape potential of several mutations within the HCV proteins, little is known about the effect of genotype/subtype-specific gene polymorphism on T-cell immunity. Therefore, this study employed several methods to examine the impact of genotype/subtype-specific polymorphisms in Core, NS3, NS5A, and NS5B sequences on T cell epitope processing and HLA-epitope interactions.
View Article and Find Full Text PDFJ Cheminform
January 2025
School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.
G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.
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