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Find video protocols related to scientific articles indexed in Pubmed.
Mechanistic study of broadly neutralizing human monoclonal antibodies against dengue virus that target the fusion loop.
J. Virol.
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There are no available vaccines for dengue, the most important mosquito-transmitted viral disease. Mechanistic studies with anti-dengue virus (DENV) human monoclonal antibodies (hMAbs) provide a rational approach to identify and characterize neutralizing epitopes on DENV structural proteins that can serve to inform vaccine strategies. Here, we report a class of hMAbs that is likely to be an important determinant in the human humoral response to DENV infection. In this study, we identified and characterized three broadly neutralizing anti-DENV hMAbs: 4.8A, D11C, and 1.6D. These antibodies were isolated from three different convalescent patients with distinct histories of DENV infection yet demonstrated remarkable similarities. All three hMAbs recognized the E glycoprotein with high affinity, neutralized all four serotypes of DENV, and mediated antibody-dependent enhancement of infection in Fc receptor-bearing cells at subneutralizing concentrations. The neutralization activities of these hMAbs correlated with a strong inhibition of virus-liposome and intracellular fusion, not virus-cell binding. We mapped epitopes of these antibodies to the highly conserved fusion loop region of E domain II. Mutations at fusion loop residues W101, L107, and/or G109 significantly reduced the binding of the hMAbs to E protein. The results show that hMAbs directed against the highly conserved E protein fusion loop block viral entry downstream of virus-cell binding by inhibiting E protein-mediated fusion. Characterization of hMAbs targeting this region may provide new insights into DENV vaccine and therapeutic strategies.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.