# NIH grant: Genetic and cellular mechanisms underlying resistance to gammaherpesvirus infection (1F31AI194775-01A1) — retained record

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[original source ↗](https://reporter.nih.gov/project-details/1F31AI194775-01A1)

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NIH grant 1F31AI194775-01A1: Genetic and cellular mechanisms underlying resistance to gammaherpesvirus infection
Institute: {'code': 'AI', 'abbreviation': 'NIAID', 'name': 'National Institute of Allergy and Infectious Diseases', 'admin_org_id': '12500003', 'admin_funding_url': ''}. PI: Courtney Marie Waytashek. Award: $34654.
Start: 2026-09-30T00:00:00.
Project Summary
Chronic infection with the gammaherpesvirus Epstein-Barr virus (EBV) represents a risk factor for several
cancers and autoimmune diseases. However, the role of host genetic variation in the regulation of immune
responses to chronic gammaherpesvirus infection and control of viral replication remains unclear. We
previously identified the wild-derived mouse strain PWD/PhJ (PWD) to have superior control of viral load
compared to C57BL/6J (B6) mice at both early and late time points during infection with MHV-68, a murine
gammaherpesvirus homologous to EBV. To further examine the role of host genetics on gammaherpesvirus
infection outcomes, we completed a genetic screen of C57BL/6J-ChrPWD chromosome substitution (consomic)
mice and identified C57BL/6J-Chr17PWD (Chr17FPWD) consomic mice as a strain having superior viral control
compared to B6 mice. We further narrowed the locus on Chr 17 to an interval at 27.6-49.4 Mb, which includes
the MHC locus. The focus of this proposal is to 1) Refine the Chr 17 locus regulating MHV-68 control and 2)
Investigate the dynamics of MHV-68 B cell infection and latency in genetically resistant hosts. In direct support
of the proposed studies, the training plan will develop knowledge, expertise, scientific communication skills,
and technical abilities in 1) using forward genetics to establish novel mouse models of gammaherpesvirus
infection, 2) immunology, with a focus on B cell and germinal center dynamics, and 3) bioinformatics with a
focus on genetic mapping via quantitative trait locus analysis. The training environment at the University of
Vermont (UVM) is ideally suited to support this proposal as it fosters interdisciplinary collaboration, provides
access to state-of-the-art facilities, and stresses active mentorship.
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