# Modeling Human Rift Valley Fever Virus Disease using the Collaborative Cross Resource

> **NIH NIH R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $195,625

## Abstract

Abstract
 Rift Valley fever virus causes disease in humans and livestock throughout Africa and into the Middle
East. It is an arbovirus of both clinical and agricultural significance that is designated as a high priority
pathogen for research and development by WHO and NIAID. Currently, our understanding of the pathogenesis
of this virus is limited by the lack of a mouse model in which human disease is accurately recapitulated. Human
disease is characterized by several distinct clinical syndromes, a self-limiting non-specific febrile illness,
hepatitis, hemorrhagic fever, and neurologic disease. In contrast, infection of inbred mice results in rapid
disseminated disease that is uniformly lethal. Data from human studies has demonstrated an association
between single nucleotide polymorphisms in innate immune signaling genes and variable clinical
manifestations, suggesting that host genetic differences could be contributing to disease outcome. Even within
the typical clinical manifestations seen in inbred mouse models, some variability exists in that the C57BL/6
mouse succumbs in 3-4 days while the BALB/c succumbs in 9-10 days, and this is associated with differences
in innate immune pathways. These data suggest that it is possible to identify mouse models that exhibit a
spectrum of RVFV disease phenotypes. Therefore, the genetically outbred Collaborative Cross resource
provides an ideal system in which to identify more varied mouse models for RVFV pathogenesis studies.
 In the proposed studies we will evaluate various strains of mice for clinical, immunological and virologic
differences. We will use a panel of biomarkers that we previously defined in humans infected with RVFV and
evaluate their utility in predicting disease manifestations in the mouse model. Using mouse strains of CC mice
that exhibit variable susceptibility or disease manifestations following RVFV infection, we will validate the
biomarker panel for predicting disease. We anticipate that these studies will identify a mouse model that better
recapitulates human disease as well as a biomarker panel that reflects the pathophysiology at work in the
disease process. Finally, if this biomarker panel proves useful in predicting disease outcome it could be further
developed as a trigger for therapeutic intervention or for use as a way to monitor response to therapy in
humans.

## Key facts

- **NIH application ID:** 9941026
- **Project number:** 5R21AI145352-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Anita K McElroy
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $195,625
- **Award type:** 5
- **Project period:** 2019-06-04 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9941026

## Citation

> US National Institutes of Health, RePORTER application 9941026, Modeling Human Rift Valley Fever Virus Disease using the Collaborative Cross Resource (5R21AI145352-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9941026. Licensed CC0.

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