West Nile virus (WNV) is an emerging neuroinvasive flavivirus that now causes significant morbidity and mortality worldwide. The innate and adaptive immune responses to WNV infection have been well studied in C57BL/6J inbred mice, but this model lacks the variations in susceptibility, immunity, and outcome to WNV infection that are observed in humans, thus limiting its usefulness to understand the mechanisms of WNV infection and immunity dynamics. To build a model of WNV infection that captures human infection outcomes, we have used the Collaborative Cross (CC) mouse model. We show that this model, which recapitulates the genetic diversity of the human population, demonstrates diversity in susceptibility and outcomes of WNV infection observed in humans. Using multiple F1 crosses of CC mice, we identified a wide range of susceptibilities to infection, as demonstrated through differences in survival, clinical disease score, viral titer, and innate and adaptive immune responses in both peripheral tissues and the central nervous system. Additionally, we examined the Oas1b alleles in the CC mice and confirmed the previous finding that Oas1b plays a role in susceptibility to WNV; however, even within a given Oas1b allele status, we identified a wide range of strain-specific WNV-associated phenotypes. These results confirmed that the CC model is effective for identifying a repertoire of host genes involved in WNV resistance and susceptibility. The CC effectively models a wide range of WNV clinical, virologic, and immune phenotypes, thus overcoming the limitations of the traditional C57BL/6J model, allowing genetic and mechanistic studies of WNV infection and immunity in differently susceptible populations.

Summary The Collaborative Cross (CC) is a panel of reproducible recombinant inbred mouse strains with high levels of standing genetic variation, thereby affording unprecedented opportunity to perform experiments in a small animal model containing controlled genetic diversity while allowing for genetic replicates. Here, we advance the utility of this unique mouse resource for immunology research, as it allows for both examination and genetic dissection of mechanisms behind adaptive immune states in mice with distinct and defined genetic makeups. This approach is founded on quantitative trait locus mapping: identifying genetically variant genome regions associated with phenotypic variance in traits-of-interest. Furthermore, the CC can be utilized for mouse model development; distinct strains have unique immunophenotypes and immune properties, making them suitable for research on particular diseases and infections. Here, we describe variation in cellular immune phenotypes across F1 crosses of CC strains, and reveal quantitative trait loci responsible for several immune phenotypes.

Infection with West Nile virus (WNV) leads to a range of disease outcomes, including chronic infection, though lack of a robust mouse model of chronic WNV infection has precluded identification of the immune events contributing to persistent infection. Using the Collaborative Cross, a population of recombinant inbred mouse strains with high levels of standing genetic variation, we have identified a mouse model of persistent WNV disease, with persistence of viral loads within the brain. Compared to lines exhibiting no disease or marked disease, the F1 cross CC(032x013)F1 displays a strong immunoregulatory signature upon infection that correlates with restraint of the WNV-directed cytolytic response. We hypothesize that this regulatory T cell response sufficiently restrains the immune response such that a chronic infection can be maintained in the CNS. Use of this new mouse model of chronic neuroinvasive virus will be critical in developing improved strategies to prevent prolonged disease in humans.

The COVID-19 pandemic has revealed that infection with SARS-CoV-2 can result in a wide range of clinical outcomes in humans. An incomplete understanding of immune correlates of protection represents a major barrier to the design of vaccines and therapeutic approaches to prevent infection or limit disease. This deficit is largely due to the lack of prospectively collected, pre-infection samples from indiviuals that go on to become infected with SARS-CoV-2. Here, we utilized data from genetically diverse Collaborative Cross (CC) mice infected with SARS-CoV to determine whether baseline T cell signatures are associated with a lack of viral control and severe disease upon infection. SARS-CoV infection of CC mice results in a variety of viral load trajectories and disease outcomes. Overall, a dysregulated, pro-inflammatory signature of circulating T cells at baseline was associated with severe disease upon infection. Our study serves as proof of concept that circulating T cell signatures at baseline can predict clinical and virologic outcomes upon SARS-CoV infection. Identification of basal immune predictors in humans could allow for identification of individuals at highest risk of severe clinical and virologic outcomes upon infection, who may thus most benefit from available clinical interventions to restrict infection and disease.

Background Virus infections result in a range of clinical outcomes for the host, from asymptomatic to severe or even lethal disease. Despite global efforts to prevent and treat virus infections to limit morbidity and mortality, the continued emergence and re-emergence of new outbreaks as well as common infections such as influenza persist as a health threat. Challenges to the prevention of severe disease after virus infection include both a paucity of protective vaccines, as well as the early identification of individuals with the highest risk that may require supportive treatment. Methods We completed a screen of mice from the Collaborative Cross (CC) that we infected with influenza, SARS-coronavirus, and West Nile virus. Results CC mice exhibited a range of disease manifestations upon infections, and we used this natural variation to identify strains with mortality following infection and strains exhibiting no mortality. We then used comprehensive pre-infection immunophenotyping to identify global baseline immune correlates of protection from mortality to virus infection. Conclusions These data suggest that immune phenotypes might be leveraged to identify humans at highest risk of adverse clinical outcomes upon infection, who may most benefit from intensive clinical interventions, in addition to providing insight for rational vaccine design.