The goal of this proposed study is to delineate the dynamics of SIV transmission from cervicovaginal (CV) exposure to establishment of systemic infection. This goal will be accomplished by developing mathematical models motivated and validated by ground-breaking observations and data from the Keele Lab that show, for the first time, tissue-level dynamics of discriminable but phenotypically identical SIV clones in a nonhuman primate (NHP) model following CV exposure to the virus. Previous work in HIV-1 transmission shows that from a large, and often diverse infecting inoculum, only a small founder population is successful in establishing systemic infection. HIV are most vulnerable to interventions early in the eclipse phase, i.e., the period between host exposure to virus and detectable viremia. But eclipse-phase dynamics are complex, cannot be directly studied in humans, and remain poorly understood. The models developed in this proposed study will be used to characterize the eclipse phase based on the tissue-level SIV data from the Keele Lab. The mathematical model will be developed in stages, iteratively refining models describing infection, from virus/cell interaction at the site of exposure in CV tissue, to dissemination of virus to lymphoid tissue and establishment of systemic infection. Importantly, while models increase in complexity, each sub-model will answer important specific questions. For example, the infected cell burst size in vivo will be quantified to address the ?superspreader? hypothesis, that initial infection dynamics are driven by the small fraction of infected cells that produce significantly more virus than the average. This will lead to an estimation of the basic reproduction number R0 in CV tissues, which is currently unknown. Indirect evidence suggests that phenotypes such as interferon (IFN) resistance and high replicative capacity may be advantageous for selection of founder virus. However no current experimental apparatus or design exists to directly assess the relative contribution of these phenotypes to transmissibility. In silico competition experiments will therefore be used to test the hypothesis that the sparsity of target cells in early CV infection selects for virus variants with fast replication kinetics, but that as IFN responses become stronger and target cells numbers increase, selection pressure shifts to favor IFN resistant variants. Since NHP models are often relied upon to guide public health measures regarding HIV when direct observations are unavailable, the improved characterization of the earliest events of SIV infection proposed will facilitate the development of better HIV prevention measures, such as novel prophylactic strategies for women. This interdisciplinary proposal bridges leading expertise in theoretical and experimental biology to develop theory that will fundamentally transform current understanding of HIV CV transmission.

Public Health Relevance

We aim to delineate the dynamics of SIV transmission, from cervicovaginal (CV) exposure, to establishment of systemic infection. Hypotheses will be investigated via mathematical models, and outcomes validated by ground- breaking data from the Keele lab which show, for the first time, the tissue-level dynamics of discriminable but phenotypically identical SIV clones during early infection. Improved characterization of the earliest events of SIV infection will facilitate the development of more efficient HIV prevention measures, since nonhuman primate models are relied upon to guide public health measures regarding HIV when direct observations, such as tissue- level viral dynamics following exposure, are unavailable.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21AI143443-02
Application #
10124269
Study Section
HIV Comorbidities and Clinical Studies Study Section (HCCS)
Program Officer
Novak, Leia Kaye
Project Start
2020-03-09
Project End
2022-02-28
Budget Start
2021-03-01
Budget End
2022-02-28
Support Year
2
Fiscal Year
2021
Total Cost
Indirect Cost
Name
Pennsylvania State University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
003403953
City
University Park
State
PA
Country
United States
Zip Code
16802