# Identification of Genetic Markers of Susceptibility to Intracellular Bacterial Infection Using the Collaborative Cross Mouse Model

> **NIH NIH R21** · GENEVA FOUNDATION · 2022 · $215,433

## Abstract

PROJECT SUMMARY
Identification of the genetic and molecular mechanisms governing immunity against intracellular bacteria is
imperative for understanding the host-pathogen-interplay and forms the basis for the development of therapeutic
countermeasures. Previous attempts at increasing our understanding of this topic have relied on targeted
interruption of individual genes or analysis natural genetic variability in natural populations. Herein, we propose
to employ 1) animal models with pre-defined genetic variability, 2) cutting edge immunoprofiling, 3) comparative
genomics, and 4) computational analyses to identify the immunological and genetic basis of sensitivity to
Rickettsia infection. This approach employs the collaborative cross (CC) mice. This mouse resource involves a
cohort of recombinant-inbred lines generated by randomizing the genetic diversity of existing inbred mouse
resources. This pre-defined genetic diversity has significantly accelerated discovery of genetic determinants that
regulate immunity against several pathogens as well as other non-infectious diseases. The CC mouse resource
is distinct from other animal models as its high genetic diversity is comparable to that of human populations.
Unlike in outbred animal models, each CC line reproducibly exhibits distinct phenotypes of disease susceptibility
and immune profiles to pathogens. Our multi-disciplinary team will screen CC lines to establish the range of
responses to the tick-borne human pathogen Rickettsia conorii. Using murine models of Rickettsia infection with
well-established phenotypic difference in susceptibility to infection, we will screen initially CC mouse lines to
encompass a detailed assessment of the disease phenotype (bacterial load, weight loss, body temperature,
survival) and immunoprofiling of peripheral blood, spleen, and liver as relevant, representative organ. CC lines
with extreme clinical and immunological phenotypes will then be selected for longitudinal in-depth
immunoprofiling. Here, changes in the frequency of activated innate and antigen-specific adaptive cells, cytokine
profiles in serum, and antibacterial activities of immune cells will be assessed throughout infection and disease
resolution. Computational data integration and bioinformatics tools (machine learning) will be applied to establish
the immune landscape of Rickettsia-specific immune responses to identify immune correlates that govern
disease phenotype of each CC line. The short-term impact of the proposed work will be the identification of novel
murine models that emulate differential immune responses to infection. These tools will enable researchers to
test therapeutics and/or vaccines in a diverse system that, for the first time, has the potential to forecast
responses in humans. Computational analysis will be performed to identify quantitative trait loci associated with
disease phenotype and disease-specific immunoprofiles. This information will be the basis for the future
identif...

## Key facts

- **NIH application ID:** 10511530
- **Project number:** 1R21AI171817-01
- **Recipient organization:** GENEVA FOUNDATION
- **Principal Investigator:** Elke BergmannLeitner
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $215,433
- **Award type:** 1
- **Project period:** 2022-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10511530

## Citation

> US National Institutes of Health, RePORTER application 10511530, Identification of Genetic Markers of Susceptibility to Intracellular Bacterial Infection Using the Collaborative Cross Mouse Model (1R21AI171817-01). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10511530. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*