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Computational analysis of antibody dynamics identifies recent HIV-1 infection

dc.contributor.authorSeaton, Kelly E.en_ZA
dc.contributor.authorVandergrift, Nathan A.en_ZA
dc.contributor.authorDeal, Aaron W.en_ZA
dc.contributor.authorRountree, Wesen_ZA
dc.contributor.authorBainbridge, Johnen_ZA
dc.contributor.authorGrebe, Eduarden_ZA
dc.contributor.authorAnderson, David A.en_ZA
dc.contributor.authorSawant, Sheetalen_ZA
dc.contributor.authorShen, Xiaoyingen_ZA
dc.contributor.authorYates, Nicole L.en_ZA
dc.contributor.authorDenny, Thomas N.en_ZA
dc.contributor.authorLiao, Hua-Xinen_ZA
dc.contributor.authorHaynes, Barton F.en_ZA
dc.contributor.authorRobb, Merlin L.en_ZA
dc.contributor.authorParkin, Neilen_ZA
dc.contributor.authorSantos, Breno R.en_ZA
dc.contributor.authorGarrett, Nigelen_ZA
dc.contributor.authorPrice, Matthew A.en_ZA
dc.contributor.authorNaniche, Deniseen_ZA
dc.contributor.authorDuerr, Ann C.en_ZA
dc.contributor.authorThe CEPHIA groupen_ZA
dc.contributor.authorKeating, Sheilaen_ZA
dc.contributor.authorHampton, Dylanen_ZA
dc.contributor.authorFacente, Shelleyen_ZA
dc.contributor.authorMarson, Karaen_ZA
dc.contributor.authorWelte, Alexen_ZA
dc.contributor.authorPilcher, Christopher D.en_ZA
dc.contributor.authorCohen, Myron S.en_ZA
dc.contributor.authorTomaras, Georgia D.en_ZA
dc.date.accessioned2019-10-01T09:46:16Z
dc.date.available2019-10-01T09:46:16Z
dc.date.issued2018
dc.identifier.citationSeaton, K. E., et al. 2018. Computational analysis of antibody dynamics identifies recent HIV-1 infection. JCI Insight, 2(24):e94355, doi:10.1172/jci.insight.94355
dc.identifier.issn2379-3708 (online)
dc.identifier.otherdoi:10.1172/jci.insight.94355
dc.identifier.urihttp://hdl.handle.net/10019.1/106549
dc.descriptionCITATION: Seaton, K. E., et al. 2018. Computational analysis of antibody dynamics identifies recent HIV-1 infection. JCI Insight, 2(24):e94355, doi:10.1172/jci.insight.94355.
dc.descriptionThe original publication is available at https://insight.jci.org
dc.description.abstractAccurate HIV-1 incidence estimation is critical to the success of HIV-1 prevention strategies. Current assays are limited by high false recent rates (FRRs) in certain populations and a short mean duration of recent infection (MDRI). Dynamic early HIV-1 antibody response kinetics were harnessed to identify biomarkers for improved incidence assays. We conducted retrospective analyses on circulating antibodies from known recent and longstanding infections and evaluated binding and avidity measurements of Env and non-Env antigens and multiple antibody forms (i.e., IgG, IgA, IgG3, IgG4, dIgA, and IgM) in a diverse panel of 164 HIV-1–infected participants (clades A, B, C). Discriminant function analysis identified an optimal set of measurements that were subsequently evaluated in a 324-specimen blinded biomarker validation panel. These biomarkers included clade C gp140 IgG3, transmitted/founder clade C gp140 IgG4 avidity, clade B gp140 IgG4 avidity, and gp41 immunodominant region IgG avidity. MDRI was estimated at 215 day or alternatively, 267 days. FRRs in untreated and treated subjects were 5.0% and 3.6%, respectively. Thus, computational analysis of dynamic HIV-1 antibody isotype and antigen interactions during infection enabled design of a promising HIV-1 recency assay for improved cross-sectional incidence estimation.en_ZA
dc.description.urihttps://insight.jci.org/articles/view/94355
dc.format.extent12 pages
dc.language.isoen_ZAen_ZA
dc.publisherAmerican Society for Clinical Investigation
dc.subjectHIV-infections -- Preventionen_ZA
dc.subjectAntibodies -- Analysisen_ZA
dc.subjectBiochemical markersen_ZA
dc.titleComputational analysis of antibody dynamics identifies recent HIV-1 infectionen_ZA
dc.typeArticleen_ZA
dc.description.versionPublisher's version
dc.rights.holderAmerican Society for Clinical Investigation


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