This proposal is a renewal application of the parent R01 project (AI095066). This application builds upon our pioneering accomplishments made during the parent period wherein we successfully demonstrated that genomic signatures within the intrahost HIV sequence population are precise and robust markers to distinguish incident from chronic infections. These genomic biomarkers, measuring the presence of closely related strains as a signature of incidence, showed over 95% accuracy. We further refined the incidence assay by implementing cutting-edge technology to design a high-throughput, next-generation sequencing platform. This assay is currently being actively tested on over 600 incident and chronic specimens in collaboration with preeminent centers with well-established cohorts. In the present application, we propose to develop a highly accurate and low cost genomic assay which concurrently determines HIV incidence and profiles transmitted drug resistance mutations (TDRMs) from a single measure in cross-sectional surveys. Major innovations of the proposal include i) integrating a high-throughput next-generation sequencing platform with a bioinformatics pipeline, statistical tests and mathematical modeling, ii) designing novel statistical methods to objectively determine the False Recency Rate (FRR) and mean duration of recent infection (MDRI) - the standard incidence assay evaluation criteria - and iii) distributing easily accessible web-based software with which the global HIV community can interact for incidence and TDRM surveillance. Our genomic assay will first produce next-generation sequencing reads of envelope gene segments to report the stage of infection. Using our new statistical hierarchical Bayesian models, we will analyze the genomic biomarker dynamics over time to calculate the MDRI and FRR. We will then monitor the presence of 82 WHO-listed commonly occurring TDRMs associated with the Department of Health and Human Services' recommended and alternative therapy regimens. Our single assay approach responds to the growing need to precisely identify TDRMs as their global presence increases. The validity of the proposed assay will be extensively tested with a large volume of HIV sequences from diverse populations, including i) well-established cohorts from preeminent centers (WIHS, CDC, CHAVI, and CEPHIA), ii) our own cohorts at the Rand Schrader Clinic at USC and Los Angeles Gay and Lesbian center, and iii) the virologic failure cohort (A5202) from the Clinical AIDS Trial Group. This project's end-product, web-based software, will systematically produce and organize vital information to inform optimal therapy regimens for individuals and geographically trace population-wide incidence and TDRM trends. The proposed work will advance HIV prevention efforts by providing a working incidence and TDRM surveillance assay ready for direct use in cross-sectional epidemic monitoring.

Public Health Relevance

This project proposes to devise a single measure assay to detect HIV incidence and screen for transmitted drug resistant viruses using high-throughput next-generation sequencing, statistical tests, and mathematical modeling. This project is expected to have important utility in helping to design novel and effective HIV prevention and intervention schemes.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-AARR-M (81))
Program Officer
Sharma, Usha K
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Southern California
Schools of Medicine
Los Angeles
United States
Zip Code
Park, Sung Yong; Love, Tanzy M T; Reynell, Lucy et al. (2017) The HIV Genomic Incidence Assay Meets False Recency Rate and Mean Duration of Recency Infection Performance Standards. Sci Rep 7:7480
Park, Sung Yong; Love, Tanzy M T; Perelson, Alan S et al. (2016) Molecular clock of HIV-1 envelope genes under early immune selection. Retrovirology 13:38
Park, Sung Yong; Mack, Wendy J; Lee, Ha Y (2016) Enhancement of viral escape in HIV-1 Nef by STEP vaccination. AIDS 30:2449-2458
Love, Tanzy M T; Park, Sung Yong; Giorgi, Elena E et al. (2016) SPMM: estimating infection duration of multivariant HIV-1 infections. Bioinformatics 32:1308-15
Park, Sung Yong; Goeken, Nolan; Lee, Hyo Jin et al. (2014) Developing high-throughput HIV incidence assay with pyrosequencing platform. J Virol 88:2977-90
Park, Min-Sun; Park, Sung Yong; Miller, Keith R et al. (2013) Accurate structure prediction of peptide-MHC complexes for identifying highly immunogenic antigens. Mol Immunol 56:81-90
Park, Sung Yong; Love, Tanzy M T; Nelson, Jeremy et al. (2011) Designing a genome-based HIV incidence assay with high sensitivity and specificity. AIDS 25:F13-9