Computational analysis of antibody dynamics identifies recent HIV-1 infection.
| dc.contributor.author | Seaton, Kelly E | |
| dc.contributor.author | Vandergrift, Nathan A | |
| dc.contributor.author | Deal, Aaron W | |
| dc.contributor.author | Rountree, Wes | |
| dc.contributor.author | Bainbridge, John | |
| dc.contributor.author | Grebe, Eduard | |
| dc.contributor.author | Anderson, David A | |
| dc.contributor.author | Sawant, Sheetal | |
| dc.contributor.author | Shen, Xiaoying | |
| dc.contributor.author | Yates, Nicole L | |
| dc.contributor.author | Denny, Thomas N | |
| dc.contributor.author | Liao, Hua-Xin | |
| dc.contributor.author | Haynes, Barton F | |
| dc.contributor.author | Robb, Merlin L | |
| dc.contributor.author | Parkin, Neil | |
| dc.contributor.author | Santos, Breno R | |
| dc.contributor.author | Garrett, Nigel | |
| dc.contributor.author | Price, Matthew A | |
| dc.contributor.author | Naniche, Denise | |
| dc.contributor.author | Duerr, Ann C | |
| dc.contributor.author | CEPHIA group | |
| dc.contributor.author | Keating, Sheila | |
| dc.contributor.author | Hampton, Dylan | |
| dc.contributor.author | Facente, Shelley | |
| dc.contributor.author | Marson, Kara | |
| dc.contributor.author | Welte, Alex | |
| dc.contributor.author | Pilcher, Christopher D | |
| dc.contributor.author | Cohen, Myron S | |
| dc.contributor.author | Tomaras, Georgia D | |
| dc.date.accessioned | 2021-01-04T22:03:32Z | |
| dc.date.available | 2021-01-04T22:03:32Z | |
| dc.date.issued | 2017-12-21 | |
| dc.date.updated | 2021-01-04T22:03:31Z | |
| dc.description.abstract | Accurate 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. | |
| dc.identifier | 94355 | |
| dc.identifier.issn | 2379-3708 | |
| dc.identifier.issn | 2379-3708 | |
| dc.identifier.uri | ||
| dc.language | eng | |
| dc.publisher | American Society for Clinical Investigation | |
| dc.relation.ispartof | JCI insight | |
| dc.relation.isversionof | 10.1172/jci.insight.94355 | |
| dc.subject | CEPHIA group | |
| dc.subject | Humans | |
| dc.subject | HIV-1 | |
| dc.subject | HIV Infections | |
| dc.subject | Immunoglobulin G | |
| dc.subject | HIV Antibodies | |
| dc.subject | HIV Antigens | |
| dc.subject | Incidence | |
| dc.subject | Retrospective Studies | |
| dc.subject | Computational Biology | |
| dc.subject | Antigen-Antibody Reactions | |
| dc.subject | Antibody Affinity | |
| dc.subject | Time Factors | |
| dc.subject | Biomarkers | |
| dc.title | Computational analysis of antibody dynamics identifies recent HIV-1 infection. | |
| dc.type | Journal article | |
| duke.contributor.orcid | Shen, Xiaoying|0000-0001-8076-1931|0000-0002-8387-3952 | |
| duke.contributor.orcid | Tomaras, Georgia D|0000-0001-8076-1931 | |
| pubs.issue | 24 | |
| pubs.organisational-group | School of Medicine | |
| pubs.organisational-group | Duke Human Vaccine Institute | |
| pubs.organisational-group | Duke Global Health Institute | |
| pubs.organisational-group | Medicine, Duke Human Vaccine Institute | |
| pubs.organisational-group | Duke | |
| pubs.organisational-group | Institutes and Centers | |
| pubs.organisational-group | University Institutes and Centers | |
| pubs.organisational-group | Institutes and Provost's Academic Units | |
| pubs.organisational-group | Medicine | |
| pubs.organisational-group | Clinical Science Departments | |
| pubs.organisational-group | Duke Cancer Institute | |
| pubs.organisational-group | Immunology | |
| pubs.organisational-group | Basic Science Departments | |
| pubs.organisational-group | Surgery, Surgical Sciences | |
| pubs.organisational-group | Surgery | |
| pubs.organisational-group | Molecular Genetics and Microbiology | |
| pubs.organisational-group | Duke Innovation & Entrepreneurship | |
| pubs.organisational-group | Initiatives | |
| pubs.publication-status | Published | |
| pubs.volume | 2 |