Homeodomain-interacting protein kinase 2 (HIPK2) is critically involved in the progression of renal fibrosis. This study aims to identify and characterize a novel HIPK2 inhibitor, CHR-6494, and investigate its therapeutic potential. Using structure-based virtual screening and molecular dynamics simulations, we identified CHR-6494 as a potent HIPK2 inhibitor with an IC of 0.97 μM. The effects of CHR-6494 on the phosphorylation of p53 in Normal Rattus norvegicus kidney cells (NRK-49F) induced by transforming growth factor-β (TGF-β) were assessed, along with its impact on TGF-β signaling and downstream profibrotic markers. CHR-6494 significantly reduces p53 phosphorylation induced by TGF-β and enhances the interaction between HIPK2 and seven in absentia 2 (SIAH2), facilitating HIPK2 degradation via proteasomal pathways. Both CHR-6494 and Abemaciclib inhibit NRK-49F cell proliferation and migration induced by TGF-β, suppressing TGF-β/Smad3 signaling and decreasing profibrotic markers such as Fibronectin I (FN-I) Collagen I and α-smooth muscle actin (α-SMA). Additionally, these compounds inhibit nuclear factor kappa-B (NF-κB) signaling and reduce inflammatory cytokine expression. The study highlights the dual functionality of HIPK2 kinase inhibitors like CHR-6494 and Abemaciclib as promising therapeutic candidates for renal fibrosis and inflammation. The findings provide new insights into HIPK2 inhibition mechanisms and suggest pathways for the design of novel HIPK2 inhibitors in the future.
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http://dx.doi.org/10.3390/ph17111420 | DOI Listing |
Sci Rep
December 2024
Department of Chemical and Biological Engineering, Gachon University, Seongnam, 13120, Republic of Korea.
The Crimean Congo virus has been reported to be a part of the spherical RNA-enveloped viruses from the Bunyaviridae family. Crimean Congo fever (CCHF) is a fatal disease with having fatality rate of up to 40%. It is declared endemic by the World Health Organization.
View Article and Find Full Text PDFNat Commun
December 2024
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China.
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency.
View Article and Find Full Text PDFJ Biomol Struct Dyn
December 2024
Department of Biotechnology, School of Bioengineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamilnadu, India.
Antimicrobial Resistance poses a major threat to human health worldwide. Microorganisms develop multi-drug resistance due to intrinsic factors, evolutionary chromosomal alterations, and horizontal gene transfer. , a common nosocomial bacterium, can cause various infections and is classified as multidrug-resistant.
View Article and Find Full Text PDFAlcohol Clin Exp Res (Hoboken)
December 2024
Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.
Background: Researchers have long been interested in identifying objective markers for problem drinking susceptibility informed by the environments in which individuals drink. However, little is known of objective cognitive-behavioral indices relevant to the social contexts in which alcohol is typically consumed. Combining group-based alcohol administration, eye-tracking technology, and longitudinal follow-up over a 2-year span, the current study examined the role of social attention in predicting patterns of problem drinking over time.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing 100020, China.
Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discovery, repurposing clinically approved or investigational drugs for the treatment of tuberculosis by computational methods has become an attractive strategy. In this study, we developed a virtual screening workflow that combines multiple machine learning and deep learning models, and 11 576 compounds extracted from the DrugBank database were screened against Mtb.
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