Browsing by Author "Chen, Chun"

Now showing 1 - 4 of 4
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    Myeloid cell-derived inducible nitric oxide synthase suppresses M1 macrophage polarization
    Lu, Geming; Zhang, Ruihua; Geng, Shuo; Peng, Liang; Jayaraman, Padmini; Chen, Chun; Xu, Feifong; Yang, Jianjun; Li, Qin; Zheng, Hao; Shen, Kimberly; Wang, Juan; Liu, Xiyu; Wang, Weidong; Zheng, Zihan; Qi, Chen-Feng; Si, Chuanping; He, John Cijiang; Liu, Kebin; Lira, Sergio A.; Sikora, Andrew G.; Li, Liwu; Xiong, Huabao (Nature, 2015-03-27)
    Here we show that iNOS-deficient mice display enhanced classically activated M1 macrophage polarization without major effects on alternatively activated M2 macrophages. eNOS and nNOS mutant mice show comparable M1 macrophage polarization compared with wild-type control mice. Addition of N6-(1-iminoethyl)-L-lysine dihydrochloride, an iNOS inhibitor, significantly enhances M1 macrophage polarization while S-nitroso-N-acetylpenicillamine, a NO donor, suppresses M1 macrophage polarization. NO derived from iNOS mediates nitration of tyrosine residues in IRF5 protein, leading to the suppression of IRF5-targeted M1 macrophage signature gene activation. Computational analyses corroborate a circuit that fine-tunes the expression of IL-12 by iNOS in macrophages, potentially enabling versatile responses based on changing microenvironments. Finally, studies of an experimental model of endotoxin shock show that iNOS deficiency results in more severe inflammation with an enhanced M1 macrophage activation phenotype. These results suggest that NO derived from iNOS in activated macrophages suppresses M1 macrophage polarization.
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    The persistence of low-grade inflammatory monocytes contributes to aggravated Atherosclerosis
    Geng, Shuo; Chen, Keqiang; Yuan, Ruoxi; Maitra, Urmila; Na, Diao; Chen, Chun; Zhang, Yao; Li, Liwu; Xiong, Huabao; Peng, Liang; Hu, Yuan; Qi, Chen-Feng; Pierce, Susan; Ling, Wenhua (Nature, 2016-11-08)
    Sustained low-grade inflammation mediated by non-resolving inflammatory monocytes has long been suspected in the pathogenesis of atherosclerosis; however, the molecular mechanisms responsible for the sustainment of non-resolving inflammatory monocytes during atherosclerosis are poorly understood. Here we observe that subclinical endotoxemia, often seen in humans with chronic inflammation, aggravates murine atherosclerosis through programming monocytes into a non-resolving inflammatory state with elevated Ly6C, CCR5, MCP-1 and reduced SR-B1. The sustainment of inflammatory monocytes is due to the disruption of homeostatic tolerance through the elevation of miR-24 and reduction of the key negative-feedback regulator IRAK-M. miR-24 reduces the levels of Smad4 required for the expression of IRAK-M and also downregulates key lipid-processing molecule SR-B1. IRAK-M deficiency in turn leads to elevated miR-24 levels, sustains disruption of monocyte homeostasis and aggravates atherosclerosis. Our data define an integrated feedback circuit in monocytes and its disruption may lead to non-resolving low-grade inflammation conducive to atherosclerosis.
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    Reprogramming macrophage orientation by microRNA 146b targeting transcription factor IRF5
    Peng, Liang; Zhang, Hui; Hao, Yuanyuan; Xu, Feihong; Yang, Jianjun; Zhang, Ruihua; Lu, Geming; Zheng, Zihan; Cui, Miao; Qi, Chen-Feng; Chen, Chun; Wang, Juan; Hu, Yuan; Wang, Di; Pierce, Susan; Li, Liwu; Xiong, Huabao (2016-12)
    The regulation of macrophage orientation pathological conditions is important but still incompletely understood. Here, we show that IL-10 and Rag1 double knockout mice spontaneously develop colitis with dominant M1 macrophage phenotype, suggesting that IL-10 regulates macrophage orientation in inflammation. We demonstrate that IL-10 stimulation induced miR-146b expression, and that the expression of miR-146b was impaired in IL-10 deficient macrophages. Our data show that miR-146b targets IRF5, resulting in the regulation of macrophage activation. Furthermore, miR-146b deficient mice developed intestinal inflammation with enhanced M1 macrophage polarization. Finally, miR-146b mimic treatment significantly suppresses M1 macrophage activation and ameliorates colitis development in vivo. Collectively, the results suggest that IL-10 dependent miR-146b plays an important role in the modulation of M1 macrophage orientation. (C) 2016 The Authors. Published by Elsevier B.V.
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    Systems Biology Study of Breast Cancer Endocrine Response and Resistance
    Chen, Chun (Virginia Tech, 2013-11-08)
    As a robust system, cells can wisely choose and switch between different signaling programs according to their differentiation stages and external environments. Cancer cells can hijack this plasticity to develop drug resistance. For example, breast cancers that are initially responsive to endocrine therapy often develop resistance robustly. This process is dynamically controlled by interactions of genes, proteins, RNAs and environmental factors at multiple scales. The complexity of this network cannot be understood by studying individual components in the cell. Systems biology focuses on the interactions of basic components, so as to uncover the molecular mechanism of cell physiology with a systemic and dynamical view. Mathematical modeling as a tool in systems biology provides a unique opportunity to understand the underlying mechanisms of endocrine response and resistance in breast cancer. In Chapter 2, I focused on the experimental observations that breast cancer cells can switch between estrogen receptor α (ERα) regulated and growth factor receptor (GFR) regulated signaling pathways for survival and proliferation. A mathematical model based on the signaling crosstalk between ERα and GFR was constructed. The model successfully explains several intriguing experimental findings related to bimodal distributions of GFR proteins in breast cancer cells, which had been lacking reasonable justifications for almost two decades. The model also explains how transient overexpression of ERα promotes resistance of breast cancer cells to estrogen withdrawal. Understanding the non-genetic heterogeneity associated with this survival-signaling switch can shed light on the design of more efficient breast cancer therapies. In Chapter 3, I utilized a novel strategy to model the transitions between the endocrine response and resistance states in breast cancer cells. Using the experimentally observed estrogen sensitivity phenotypes in breast cancer (sensitive, hypersensitive, and supersensitive) as example, I proposed a useful framework of modeling cell state transitions on the energy landscape of breast cancer as a dynamical system. Grounded on the most possible routes of transitions on the breast cancer landscape, a state transition model was developed. By analyzing this model, I investigated the optimum settings of two intuitive strategies, sequential and intermittent treatments, to overcome endocrine resistance in breast cancer. The method used in this study can be generalized to study treatment strategies and improve treatment efficiencies in breast cancer as well as other types of cancer.