Dataset Information

Blood-borne macrophage-neural cell interactions hitchhike on endosome networks for cell-based nanozyme brain delivery.

ABSTRACT: Macrophage-carried nanoformulated catalase ('nanozyme') attenuates neuroinflammation and protects nigrostriatal neurons from 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine intoxication. This is facilitated by effective enzyme transfer from blood-borne macrophages to adjacent endothelial cells and neurons leading to the decomposition of reactive oxygen species.We examined the intra- and inter-cellular trafficking mechanisms of nanozymes by confocal microscopy. Improved neuronal survival mediated by nanozyme-loaded macrophages was demonstrated by fluorescence-activated cell sorting.In macrophages, nanozymes were internalized mainly by clathrin-mediated endocytosis then trafficked to recycling endosomes. The enzyme is subsequently released in exosomes facilitated by bridging conduits. Nanozyme transfer from macrophages to adjacent cells by endocytosis-independent mechanisms diffusing broadly throughout the recipient cells. In contrast, macrophage-free nanozymes were localized in lysosomes following endocytic entry.Facilitated transfer of nanozyme from cell to cell can improve neuroprotection against oxidative stress commonly seen during neurodegenerative disease processes.

SUBMITTER: Haney MJ

PROVIDER: S-EPMC3384770 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Blood-borne macrophage-neural cell interactions hitchhike on endosome networks for cell-based nanozyme brain delivery.

Haney Matthew J MJ Suresh Poornima P Zhao Yuling Y Kanmogne Georgette D GD Kadiu Irena I Sokolsky-Papkov Marina M Klyachko Natalia L NL Mosley R Lee RL Kabanov Alexander V AV Gendelman Howard E HE Batrakova Elena V EV

Nanomedicine (London, England) 20120111 6

<h4>Background</h4>Macrophage-carried nanoformulated catalase ('nanozyme') attenuates neuroinflammation and protects nigrostriatal neurons from 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine intoxication. This is facilitated by effective enzyme transfer from blood-borne macrophages to adjacent endothelial cells and neurons leading to the decomposition of reactive oxygen species.<h4>Materials & methods</h4>We examined the intra- and inter-cellular trafficking mechanisms of nanozymes by confocal mic ...[more]

PMID: 22236307

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Project description:Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

Dataset Information

Blood-borne macrophage-neural cell interactions hitchhike on endosome networks for cell-based nanozyme brain delivery.

Publications

Blood-borne macrophage-neural cell interactions hitchhike on endosome networks for cell-based nanozyme brain delivery.

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