Matching Items (2)
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Description

Two distinct monocyte (Mo)/macrophage (Mp) subsets (Ly6Clow and Ly6Chi) orchestrate cardiac recovery process following myocardial infarction (MI). Prostaglandin (PG) E2 is involved in the Mo/Mp-mediated inflammatory response, however, the role of its receptors in Mos/Mps in cardiac healing remains to be determined. Here we show that pharmacological inhibition or gene

Two distinct monocyte (Mo)/macrophage (Mp) subsets (Ly6Clow and Ly6Chi) orchestrate cardiac recovery process following myocardial infarction (MI). Prostaglandin (PG) E2 is involved in the Mo/Mp-mediated inflammatory response, however, the role of its receptors in Mos/Mps in cardiac healing remains to be determined. Here we show that pharmacological inhibition or gene ablation of the Ep3 receptor in mice suppresses accumulation of Ly6Clow Mos/Mps in infarcted hearts. Ep3 deletion in Mos/Mps markedly attenuates healing after MI by reducing neovascularization in peri-infarct zones. Ep3 deficiency diminishes CX3C chemokine receptor 1 (CX3CR1) expression and vascular endothelial growth factor (VEGF) secretion in Mos/Mps by suppressing TGFβ1 signaling and subsequently inhibits Ly6Clow Mos/Mps migration and angiogenesis. Targeted overexpression of Ep3 receptors in Mos/Mps improves wound healing by enhancing angiogenesis. Thus, the PGE2/Ep3 axis promotes cardiac healing after MI by activating reparative Ly6Clow Mos/Mps, indicating that Ep3 receptor activation may be a promising therapeutic target for acute MI.

ContributorsTang, Juan (Author) / Shen, Yujun (Author) / Chen, Guilin (Author) / Wan, Qiangyou (Author) / Wang, Kai (Author) / Zhang, Jian (Author) / Qin, Jing (Author) / Liu, Guizhu (Author) / Zuo, Shengkai (Author) / Tao, Bo (Author) / Yu, Yu (Author) / Wang, Junwen (Author) / Lazarus, Michael (Author) / Yu, Ying (Author) / College of Health Solutions (Contributor)
Created2017-03-03
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Description

Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF–TF interactions that form TF

Modeling of transcriptional regulatory networks (TRNs) has been increasingly used to dissect the nature of gene regulation. Inference of regulatory relationships among transcription factors (TFs) and genes, especially among multiple TFs, is still challenging. In this study, we introduced an integrative method, LogicTRN, to decode TF–TF interactions that form TF logics in regulating target genes. By combining cis-regulatory logics and transcriptional kinetics into one single model framework, LogicTRN can naturally integrate dynamic gene expression data and TF-DNA-binding signals in order to identify the TF logics and to reconstruct the underlying TRNs. We evaluated the newly developed methodology using simulation, comparison and application studies, and the results not only show their consistence with existing knowledge, but also demonstrate its ability to accurately reconstruct TRNs in biological complex systems.

ContributorsYan, Bin (Author) / Guan, Daogang (Author) / Wang, Chao (Author) / Wang, Junwen (Author) / He, Bing (Author) / Qin, Jing (Author) / Boheler, Kenneth R. (Author) / Lu, Aiping (Author) / Zhang, Ge (Author) / Zhu, Hailong (Author) / College of Health Solutions (Contributor)
Created2017-10-19