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Large-sized Graphene Oxide Synergistically Enhance Parenchymal Hepatocyte IL-6 Expression Monitored by Dynamic Imaging

Abstract

Graphene oxides (GOs) have received significant attention as emerging biomedical materials due to their special properties. Application of GOs in biological systems has raised considerable concerns about their hepatotoxicity, however the biological effects on parenchymal hepatocytes remain unclear, despite the fact that GO showed size-dependent interactions with immunocytes in the liver. Herein we chose pleiotropic cytokine IL-6 as the parameter to guiding inflammation responses upon GOs exposure. An early and sensitive reporter mouse model was constructed, allowing non-invasive and longitudinal imaging of parenchymal hepatocyte IL-6 expressions. GOs of various lateral dimensions were assessed by using the reporter mice. The results demonstrated that large-sized GOs (L-GO) induced much stronger IL-6 activation. Detailed analysis uncovered that L-GO induced ROS production which promoted macrophage polarization and pro-inflammation cytokines IL-1β and TNF-α secretion, activated of the NF-κB signal pathway, and in turn initiated the expression of IL-6 in hepatocytes. Especially, large-sized GO significantly enhanced parenchymal hepatocyte IL-6 expression synergistically through direct adhesion on the plasma membrane in the presence of pro-inflammation cytokines. These in-depth investigations are expected to help modulate the inflammatory responses involved in hepatotoxicity and provide extended information to design sub-hepatic distribution and cell subsets targeting by controlling the nanoparticle sizes.

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Supplementary files

Article information


Submitted
19 Dec 2019
Accepted
05 Mar 2020
First published
16 Mar 2020

Nanoscale, 2020, Accepted Manuscript
Article type
Communication

Large-sized Graphene Oxide Synergistically Enhance Parenchymal Hepatocyte IL-6 Expression Monitored by Dynamic Imaging

L. Zhan, Y. Zhang, C. Ma, Z. Wang, Q. Zhou, S. Sun, P. Ma, L. Lv, X. Jiang and X. Wang, Nanoscale, 2020, Accepted Manuscript , DOI: 10.1039/C9NR10713D

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