Diabetic nephropathy (DN) is one of the leading clinical causes of end-stage renal failure. The classical aldose reductase (AR) inhibitor epalrestat shows beneficial effect on renal dysfunction induced by DN, with metabolic profile and molecular mechanisms remains to be investigated further. In the current study, integrated untargeted metabolomics, network pharmacology and molecular dynamics approaches were applied to explore the therapeutic mechanisms of epalrestat against DN. Firstly, untargeted serum and urine metabolomics analysis based on UPLC-Q-TOF-MS was performed, revealed that epalrestat could regulate the metabolic disorders of amino acids metabolism, arachidonic acid metabolism, pyrimidine metabolism and citrate cycle metabolism pathways after DN. Subsequently, metabolomics-based network analysis was carried out to predict potential active targets of epalrestat, mainly involving AGE-RAGE signaling pathway, TNF signaling pathway and HIF-1 signaling pathway. Moreover, a 100 ns molecular dynamics approach was employed to validate the interactions between epalrestat and the core targets, showing that epalrestat could form remarkable tight binding with GLUT1 and NFκB than it with AR. Surface-plasmon resonance assay further verified epalrestat could bind GLUT1 and NFκB proteins specifically. Overall, integrated system network analysis not only demonstrated that epalrestat could attenuate DN induced metabolic disorders and renal injuries, but also revealed that it could interact with multi-targets to play a synergistic regulatory role in the treatment of DN.
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http://dx.doi.org/10.1016/j.intimp.2024.112812 | DOI Listing |
Methods Mol Biol
January 2025
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Measurements of cell phylogeny based on natural or induced mutations, known as lineage barcodes, in conjunction with molecular phenotype have become increasingly feasible for a large number of single cells. In this chapter, we delve into Quantitative Fate Mapping (QFM) and its computational pipeline, which enables the interrogation of the dynamics of progenitor cells and their fate restriction during development. The methods described here include inferring cell phylogeny with the Phylotime model, and reconstructing progenitor state hierarchy, commitment time, population size, and commitment bias with the ICE-FASE algorithm.
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January 2025
Department of Chemistry, Yuvaraja's College, University of Mysore, Manasagangotri, 570006 Mysuru, India.
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View Article and Find Full Text PDFMethods Mol Biol
January 2025
Stowers Institute for Medical Research, Kansas City, MO, USA.
Understanding the spatial and temporal dynamics of gene expression is crucial for unraveling molecular mechanisms underlying various biological processes. While traditional methods have offered insights into gene expression patterns, they primarily focus on mature mRNA transcripts, lacking real-time visualization of newly synthesized or nascent transcription events. Recent advancements in monitoring nascent transcription in live cells provide valuable insights into transcriptional dynamics.
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January 2025
Department of Clinical Laboratory, the Fourth Affiliated Hospital of School of medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Since December 2019, the global dissemination of a novel coronavirus has precipitated a notable public health crisis, prompting considerable interest and scrutiny from governmental and scholarly entities. Substantial research efforts have been dedicated to exploring diverse facets of this novel coronavirus, encompassing its pathogenesis, transmission dynamics, and therapeutic interventions. Recent findings suggest that circular RNAs (circRNAs) exert a pivotal influence on modulating viral infectivity and immune defense mechanisms.
View Article and Find Full Text PDFImmunol Rev
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Laboratory of Immunobiology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
αβT cells protect vertebrates against many diseases, optimizing surveillance using mechanical force to distinguish between pathophysiologic cellular alterations and normal self-constituents. The multi-subunit αβT-cell receptor (TCR) operates outside of thermal equilibrium, harvesting energy via physical forces generated by T-cell motility and actin-myosin machinery. When a peptide-bound major histocompatibility complex molecule (pMHC) on an antigen presenting cell is ligated, the αβTCR on the T cell leverages force to form a catch bond, prolonging bond lifetime, and enhancing antigen discrimination.
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