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Development and characterization of a bead-based, multiplex assay for estimation of recent HIV type 1 infection
Authors:Curtis Kelly A  Kennedy M Susan  Charurat Man  Nasidi Abdulsalami  Delaney Kevin  Spira Thomas J  Owen S Michele
Affiliation:Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia 30333, USA. czv2@cdc.gov
Abstract:Estimation of HIV-1 incidence is an important public health tool for understanding the status of the epidemic, identifying high-risk populations, and assessing various intervention strategies. Several laboratory-based methods have been developed for distinguishing recent from long-term HIV-1 infection; however, each exhibits some degree of misclassification, particularly among AIDS patients and those taking antiretroviral therapy (ART). To improve upon the limitations associated with measuring responses to a single analyte, we have developed a bead-based, multiplex assay for determination of HIV recent infection based on total antibody binding and antibody avidity to multiple analytes. An HIV-specific, multiplex panel was created by coupling the recombinant HIV-1 proteins p66, gp120, gp160, and gp41 to Bio-Plex COOH microspheres. Longitudinal plasma specimens from recent seroconverters were tested for reactivity to the coupled microspheres using the Bio-Plex 200 System. For each analyte, HIV-specific antibody binding and avidity increased for 1-2 years post-seroconversion, leading to a significant difference in reactivity between recent and long-term specimens. While the potential for misclassification of individuals diagnosed with AIDS or receiving ART appears to be minimal with avidity measures, the impact on total antibody binding was variable, depending on the individual analyte. This bead-based, HIV-specific multiplex assay measures several distinct immune responses in a single assay plate, allowing for sampling of multiple analytes in the determination of recent infection, which could aid in the development of improved statistical methods or algorithms that will more accurately estimate HIV incidence.
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