Rapid evolution of the neutralizing antibody response to HIV type 1 infection.
ABSTRACT A recombinant virus assay was used to characterize in detail neutralizing antibody responses directed at circulating autologous HIV in plasma. Examining serial plasma specimens in a matrix format, most patients with primary HIV infection rapidly generated significant neutralizing antibody responses to early (0-39 months) autologous viruses, whereas responses to laboratory and heterologous primary strains were often lower and delayed. Plasma virus continually and rapidly evolved to escape neutralization, indicating that neutralizing antibody exerts a level of selective pressure that has been underappreciated based on earlier, less comprehensive characterizations. These data argue that neutralizing antibody responses account for the extensive variation in the envelope gene that is observed in the early months after primary HIV infection.
SourceAvailable from: Zizhang Sheng[Show abstract] [Hide abstract]
ABSTRACT: HIV-1-neutralizing antibodies develop in most HIV-1-infected individuals, although highly effective antibodies are generally observed only after years of chronic infection. Here, we characterize the rate of maturation and extent of diversity for the lineage that produced the broadly neutralizing antibody VRC01 through longitudinal sampling of peripheral B cell transcripts over 15 years and co-crystal structures of lineage members. Next-generation sequencing identified VRC01-lineage transcripts, which encompassed diverse antibodies organized into distinct phylogenetic clades. Prevalent clades maintained characteristic features of antigen recognition, though each evolved binding loops and disulfides that formed distinct recognition surfaces. Over the course of the study period, VRC01-lineage clades showed continuous evolution, with rates of ∼2 substitutions per 100 nucleotides per year, comparable to that of HIV-1 evolution. This high rate of antibody evolution provides a mechanism by which antibody lineages can achieve extraordinary diversity and, over years of chronic infection, develop effective HIV-1 neutralization. Copyright © 2015 Elsevier Inc. All rights reserved.Cell 04/2015; 161(3). DOI:10.1016/j.cell.2015.03.004 · 33.12 Impact Factor
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ABSTRACT: The role of antibodies in HIV-1 infection is investigated using a discrete-time mathematical model that considers cell-free and cell-associated transmission of the virus. Model analysis shows that the effect of each type of antibody is dependent on the stage of the infection. Neutralizing antibodies are efficient in controlling the viral levels in the early days after seroconversion and antibodies that coat HIV-1-infected cells and recruit effector cells to either kill the HIV-1-infected cells or inhibit viral replication are efficient when the infection becomes established. Model simulations show that antibodies that inhibit viral replication are more effective in controlling the infection than those that recruit Natural Killer T cells after infection establishment. The model was fitted to subjects of the Tsedimoso study conducted in Botswana and conclusions similar to elasticity analysis results were obtained. Model fitting results predicted that neutralizing antibodies are more efficient in controlling the viral levels than antibodies that coat HIV-1-infected cells and recruit effector cells to either kill the HIV-1-infected cells or inhibit viral replication in the early days after seroconversion. © The Authors 2015. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.Mathematical Medicine and Biology 04/2015; 32(1). DOI:10.1093/imammb/dqv014 · 1.43 Impact Factor
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ABSTRACT: The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.PLoS Computational Biology 04/2015; 11(4):e1004185. DOI:10.1371/journal.pcbi.1004185 · 4.83 Impact Factor