Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Corresponding author
Oliver Laeyendecker
Laboratory of Immunoregulation, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH, Baltimore, MD, USA; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Kai Kammers
Division of Biostatistics and Bioinformatics, Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Athena Chen
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Mariya V. Sivay
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Sanjay Kottapalli
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Brandon M. Sie
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Tiezheng Yuan
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Daniel R. Monaco
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Divya Mohan
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Daniel Wansley
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Tomasz Kula
Division of Genetics, Department of Medicine, Howard Hughes Medical Institute, Brigham and Women’s Hospital, Department of Genetics, Harvard University Medical School, Boston, MA 02115, USA
Charles Morrison
FHI 360, Clinical and Epidemiologic Sciences, Durham, NC, USA
Stephen J. Elledge
Division of Genetics, Department of Medicine, Howard Hughes Medical Institute, Brigham and Women’s Hospital, Department of Genetics, Harvard University Medical School, Boston, MA 02115, USA
Ron Brookmeyer
Department of Biostatistics, University of California at Los Angeles, Los Angeles, CA, USA
Ingo Ruczinski
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
H. Benjamin Larman
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Corresponding author
Summary: This study evaluates HIV antibody responses and their evolution during the course of HIV infection. A phage display system is used to characterize antibody binding to >3,300 HIV peptides in 57 adults with early- to late-stage infection. We find that the number of unique epitopes targeted (“antibody breadth”) increases early in infection and then stabilizes or declines. A decline in antibody breadth 9 months to 2 years after infection is associated with subsequent antiretroviral treatment (ART) initiation, and a faster decline in antibody breadth is associated with a shorter time to ART initiation. We identify 266 peptides with increasing antibody reactivity over time and 43 peptides with decreasing reactivity over time. These data are used to design a prototype four-peptide “serosignature” to predict duration of HIV infection. We also demonstrate that epitope engineering can be used to optimize peptide binding properties for applications such as cross-sectional HIV incidence estimation. : Eshleman et al. quantify antibody binding to >3,300 HIV peptides from early- to late-stage infection using a phage display system (VirScan). Binding diversity (breadth) reaches individual-specific set points; breadth decline is associated with CD4 cell loss. Time-dependent binding specificities are identified, optimized, and used to predict duration of HIV infection. Keywords: antibody response to HIV, antibody profiling, HIV incidence, antibody biomarker, serosignature, immunodominant HIV epitopes
