# Project 2: Characterization of the in vivo T cell (and overall immune) interception of primary SIV infection after vaccination with differentially response programmed RhCMV/SIV vectors

> **NIH NIH P01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2022 · $755,463

## Abstract

PROJECT 2 - PROJECT SUMMARY
Vaccination of Rhesus macaques (RMs) with SIV insert-expressing 68-1 Rhesus Cytomegalovirus vectors
(RhCMV/SIV) elicits an immune response that in 59% of vaccinees can intercept and effectively arrest an early
spreading primary SIV infection. This unique pattern of “replication arrest” efficacy has been linked to three 68-
1 RhCMV/SIV immune characteristics, including its ability to generate and maintain: 1) high magnitude,
circulating and tissue-based, effector memory-biased CD8+ T cell responses, 2) major histocompatibility complex
(MHC)-E-restricted SIV-specific CD8+ T cell responses and 3) in RMs with MHC-E-restricted CD8+ T cell
responses, a protection-predictive innate immune transcriptional response to vaccination that includes a central
IL-15 signaling component; however, the specific immune mechanism(s) underlying replication arrest protection
are unknown. RhCMV/SIV vectors can now be programmed by genetic manipulation to elicit SIV-specific CD8+
T cell responses that are restricted by MHC-E-only, MHC-II-only and MHC-Ia-only, and while the latter 2
response types are similar in magnitude and phenotype to the MHC-E-restricted responses, they, as well as
conventional prime-boost vaccine elicited MHC-Ia-restricted CD8+ T cell responses, are unable to mediate SIV
replication arrest. This indicates that the protective (IL-15/innate-modulated) MHC-E-restricted SIV-specific CD8+
T cells must manifest a different response upon interception of primary SIV infection than the other T cell
response types, possibly being more efficient in detecting and responding to SIV-infected cells in vivo and/or
manifesting a different (more effective) functional program upon infected cell recognition. In this project, we will
use sophisticated `omics technologies that enable detailed characterization of tissue immune responses
(including specific analyses of T cell receptor-defined, SIV-specific CD8+ T cells) to define these differences in
vivo in the actual RM tissues hosting an early spreading SIV infection. In S.A.1, we will define the systemic viral
and immune activation trajectories of unvaccinated vs. (long-term, previously protected and functionally cured)
68-1 RhCMV/SIV vaccinated RMs by analysis of serial necropsies with the goal identifying the optimal post
challenge timepoint(s) to study the cellular immune interception of viral spread prior to progressive viremia or
stable establishment of replication arrest. In S.A.2, we will use this optimized RM model to identify the unique
characteristics of RhCMV/SIV-elicited MHC-E-restricted CD8+ T cell responses in vaccinated naïve RMs that
mediate SIV replication arrest efficacy by comparison of the viral:immune intercept of these responses vs. MHC-
II-only and MHC-Ia-only SIV-specific CD8+ T cell responses elicited in naïve RMs by differentially programmed
RhCMV vectors and vs. MHC-Ia-restricted CD8+ T cell responses elicited by the conventional ChAdOx1/MVA
vaccine. These contrast...

## Key facts

- **NIH application ID:** 10619303
- **Project number:** 1P01AI174856-01
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Scott G Hansen
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $755,463
- **Award type:** 1
- **Project period:** 2022-09-22 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10619303

## Citation

> US National Institutes of Health, RePORTER application 10619303, Project 2: Characterization of the in vivo T cell (and overall immune) interception of primary SIV infection after vaccination with differentially response programmed RhCMV/SIV vectors (1P01AI174856-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10619303. Licensed CC0.

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