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A Super TLR Agonist to Improve Efficacy of Dendritic Cell Vaccine in Induction of Anti-HCV Immunity

Overview of attention for article published in PLOS ONE, November 2012
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Title
A Super TLR Agonist to Improve Efficacy of Dendritic Cell Vaccine in Induction of Anti-HCV Immunity
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0048614
Pubmed ID
Authors

Bangxing Hong, Sung-Hyung Lee, Xiao-Tong Song, Lindsey Jones, Keigo Machida, Xue F. Huang, Si-Yi Chen

Abstract

Persistent infections caused by pathogens such as hepatitis C virus are major human diseases with limited or suboptimal prophylactic and therapeutic options. Given the critical role of dendritic cell (DC) in inducing immune responses, DC vaccination is an attractive means to prevent and control the occurrence and persistence of the infections. However, DCs are built-in with inherent negative regulation mechanisms which attenuate their immune stimulatory activity and lead to their ineffectiveness in clinical application. In this study, we developed a super DC stimulant that consists of a modified, secretory Toll-like Receptor (TLR)-5 ligand and an inhibitor of the negative regulator, suppressor of cytokine sinaling-1 (SOCS1). We found that expressing the super stimulant in DCs is drastically more potent and persistent than using the commonly used DC stimuli to enhance the level and duration of inflammatory cytokine production by both murine and human DCs. Moreover, the DCs expressing the super stimulant are more potent to provoke both cellular and humoral immune responses against hepatitis C virus (HCV) antigen in vivo. Thus, the strategy capable of triggering and sustaining proinflammatory status of DCs may be used to boost efficiency of DC vaccine in preventing and combating the persistent infection of HCV or other chronic viruses.

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Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 38%
Researcher 7 29%
Student > Master 2 8%
Professor 1 4%
Other 1 4%
Other 1 4%
Unknown 3 13%
Readers by discipline Count As %
Immunology and Microbiology 6 25%
Agricultural and Biological Sciences 6 25%
Engineering 3 13%
Medicine and Dentistry 2 8%
Biochemistry, Genetics and Molecular Biology 1 4%
Other 1 4%
Unknown 5 21%