Our recent studies developed acute sequence evolution model which has been applied to interpret sequence clones derived from 102 subjects with acute HIV subtype B infection, from 69 subjects with acute HIV subtype C infection, and from numerous SIV-infected macaques. Our model enabled an assessment of the sequence diversity in HIV infections originating from a single transmitted viral strain and the estimation on the period of infection, which has been a significant and beneficial contribution to HIV research community. We propose to expand our research activity of modeling intrahost HIV diversification toward the development of a novel assay identifying new, recent HIV infections. In the HIV/AIDS prevention field, it is critical to assess how many people have been recently infected in a given area in order to evaluate the feasibility of prevention and intervention trials. The diagnosis of HIV infection is currently possible from blood samples but reliable assays predicting how long an individual has been infected have not been developed. Various detuned antibody assays, or avidity-based assessments, have been used, assuming that antibody titer and avidity increases with time. However, serologic assays are problematic because (i) the rate of maturation of the antibody response varies between different individuals, (ii) some subjects with low CD4 counts or low virus loads may be inaccurately counted as having recent infection, and (iii) serologic assays can be negatively affected if the infecting virus clade differs from the clade of the antigen used in the assay (this is a particular problem in mixed-clade epidemics). This proposal aims to provide empirical and theoretical foundations for inventing a novel assay that distinguishes new, incident infections from chronic, asymptomatic infections. Major innovations of the proposal include (i) its comprehensive integration of next-generation ultradeep pyrosequencing data and biomathematical modeling and (ii) a novel statistical design estimating the number of founder viruses and the duration of infection. Our proposed study will focus on overcoming two primary barriers to the development of a sequencing based assay. First, the assay potentially mis-classifies early infections with multiple distinct founder strains as chronic infections. Second, it is questionable whether the assay can distinguish incident samples from chronic ones obtained at late stages of infections. We propose to collect a large scale of HIV sequence data from diverse population in different epidemic regions and different stages of infections. Ultradeep sequencing data in conjunction with novel statistical designs will lead us to invent a novel assay that is capable of identifying incident infections. The proposed work will advance HIV prevention research by providing a reliable assay based on the characteristics of intrahost HIV diversification.

Public Health Relevance

This project proposes to devise a novel assay distinguishing incidence HIV infections from chronic infections using high-throughput massive sequencing and statistical tests. This project is expected to have important utility in helping to design novel and effective HIV prevention and intervention schemes.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI095066-04
Application #
8514506
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Sharma, Usha K
Project Start
2011-08-10
Project End
2015-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
4
Fiscal Year
2013
Total Cost
$481,766
Indirect Cost
$188,078
Name
University of Southern California
Department
Microbiology/Immun/Virology
Type
Schools of Medicine
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
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