Collaborative Cross mice have diverse phenotypic responses to infection with Methicillin-resistant Staphylococcus aureus USA300.

Staphylococcus aureus (S. aureus) is an opportunistic pathogen causing diseases ranging from mild skin infections to life threatening conditions, including endocarditis, pneumonia, and sepsis. To identify host genes modulating this host-pathogen interaction, we infected 25 Collaborative Cross (CC) mouse strains with methicillin-resistant S. aureus (MRSA) and monitored disease progression for seven days using a surgically implanted telemetry system. CC strains varied widely in their response to intravenous MRSA infection. We identified eight 'susceptible' CC strains with high bacterial load, tissue damage, and reduced survival. Among the surviving strains, six with minimal colonization were classified as 'resistant', while the remaining six tolerated higher organ colonization ('tolerant'). The kidney was the most heavily colonized organ, but liver, spleen and lung colonization were better correlated with reduced survival. Resistant strains had higher pre-infection circulating neutrophils and lower post-infection tissue damage compared to susceptible and tolerant strains. We identified four CC strains with sexual dimorphism: all females survived the study period while all males met our euthanasia criteria earlier. In these CC strains, males had more baseline circulating monocytes and red blood cells. We identified several CC strains that may be useful as new models for endocarditis, myocarditis, pneumonia, and resistance to MRSA infection. Quantitative Trait Locus (QTL) analysis identified two significant loci, on Chromosomes 18 and 3, involved in early susceptibility and late survival after infection. We prioritized Npc1 and Ifi44l genes as the strongest candidates influencing survival using variant analysis and mRNA expression data from kidneys within these intervals.

Using the collaborative cross to identify the role of host genetics in defining the murine gut microbiome.

BACKGROUND: The human gut microbiota is a complex community comprised of trillions of bacteria and is critical for the digestion and absorption of nutrients. Bacterial communities of the intestinal microbiota influence the development of several conditions and diseases. We studied the effect of host genetics on gut microbial composition using Collaborative Cross (CC) mice. CC mice are a panel of mice that are genetically diverse across strains, but genetically identical within a given strain allowing repetition and deeper analysis than is possible with other collections of genetically diverse mice. RESULTS: 16S rRNA from the feces of 167 mice from 28 different CC strains was sequenced and analyzed using the Qiime2 pipeline. We observed a large variance in the bacterial composition across CC strains starting at the phylum level. Using bacterial composition data, we identified 17 significant Quantitative Trait Loci (QTL) linked to 14 genera on 9 different mouse chromosomes. Genes within these intervals were analyzed for significant association with pathways and the previously known human GWAS database using Enrichr analysis and Genecards database. Multiple host genes involved in obesity, glucose homeostasis, immunity, neurological diseases, and many other protein-coding genes located in these regions may play roles in determining the composition of the gut microbiota. A subset of these CC mice was infected with Salmonella Typhimurium. Using infection outcome data, an increase in abundance of genus Lachnospiraceae and decrease in genus Parasutterella correlated with positive health outcomes after infection. Machine learning classifiers accurately predicted the CC strain and the infection outcome using pre-infection bacterial composition data from the feces. CONCLUSION: Our study supports the hypothesis that multiple host genes influence the gut microbiome composition and homeostasis, and that certain organisms may influence health outcomes after S. Typhimurium infection. Video Abstract.

Elucidating Mechanisms of Tolerance to Salmonella Typhimurium across Long-Term Infections Using the Collaborative Cross.

Understanding the molecular mechanisms underlying resistance and tolerance to pathogen infection may present the opportunity to develop novel interventions. Resistance is the absence of clinical disease with a low pathogen burden, while tolerance is minimal clinical disease with a high pathogen burden. Salmonella is a worldwide health concern. We studied 18 strains of collaborative cross mice that survive acute Salmonella Typhimurium (STm) infections. We infected these strains orally and monitored them for 3 weeks. Five strains cleared STm (resistant), six strains maintained a bacterial load and survived (tolerant), while seven strains survived >7 days but succumbed to infection within the study period and were called "delayed susceptible." Tolerant strains were colonized in the Peyer's patches, mesenteric lymph node, spleen, and liver, while resistant strains had significantly reduced bacterial colonization. Tolerant strains had lower preinfection core body temperatures and had disrupted circadian patterns of body temperature postinfection sooner than other strains. Tolerant strains had higher circulating total white blood cells than resistant strains, driven by increased numbers of neutrophils. Tolerant strains had more severe tissue damage and higher circulating levels of monocyte chemoattractant protein 1 (MCP-1) and interferon gamma (IFN-γ), but lower levels of epithelial neutrophil-activating protein 78 (ENA-78) than resistant strains. Quantitative trait locus (QTL) analysis revealed one significant association and six suggestive associations. Gene expression analysis identified 22 genes that are differentially regulated in tolerant versus resistant animals that overlapped these QTLs. Fibrinogen genes (Fga, Fgb, and Fgg) were found across the QTL, RNA, and top canonical pathways, making them the best candidate genes for differentiating tolerance and resistance. IMPORTANCE To survive a bacterial infection, an infected host can display resistance or tolerance. Resistance is indicated by a decrease in pathogen load, while for tolerance a high pathogen load is accompanied by minimal disease. We infected genetically diverse mice with Salmonella Typhimurium for 21 days and discovered new phenotypes for disease outcome (delayed susceptible, tolerant, and resistant). Tolerant strains showed the lowest preinfection core body temperatures and the most rapid disruption in circadian patterns of body temperature postinfection. Tolerant strains had higher circulating neutrophils and higher circulating levels of MCP-1 and IFN-γ, but lower levels of ENA-78 than did resistant strains, in addition to more severe tissue damage. QTL analysis revealed multiple associated regions, and gene expression analysis identified 22 genes that are differentially regulated in tolerant versus resistant animals in these regions. Fibrinogen genes (Fga, Fgb, and Fgg) were found across the QTL, RNA, and the top canonical pathways, suggesting a role in tolerance.

Genetic background influences survival of infections with Salmonella enterica serovar Typhimurium in the Collaborative Cross.

Salmonella infections typically cause self-limiting gastroenteritis, but in some individuals these bacteria can spread systemically and cause disseminated disease. Salmonella Typhimurium (STm), which causes severe systemic disease in most inbred mice, has been used as a model for disseminated disease. To screen for new infection phenotypes across a range of host genetics, we orally infected 32 Collaborative Cross (CC) mouse strains with STm and monitored their disease progression for seven days by telemetry. Our data revealed a broad range of phenotypes across CC strains in many parameters including survival, bacterial colonization, tissue damage, complete blood counts (CBC), and serum cytokines. Eighteen CC strains survived to day 7, while fourteen susceptible strains succumbed to infection before day 7. Several CC strains had sex differences in survival and colonization. Surviving strains had lower pre-infection baseline temperatures and were less active during their daily active period. Core body temperature disruptions were detected earlier after STm infection than activity disruptions, making temperature a better detector of illness. All CC strains had STm in spleen and liver, but susceptible strains were more highly colonized. Tissue damage was weakly negatively correlated to survival. We identified loci associated with survival on Chromosomes (Chr) 1, 2, 4, 7. Polymorphisms in Ncf2 and Slc11a1, known to reduce survival in mice after STm infections, are located in the Chr 1 interval, and the Chr 7 association overlaps with a previously identified QTL peak called Ses2. We identified two new genetic regions on Chr 2 and 4 associated with susceptibility to STm infection. Our data reveal the diversity of responses to STm infection across a range of host genetics and identified new candidate regions for survival of STm infection.

Mathematical methods for visualization and anomaly detection in telemetry datasets.

Recent developments in both biological data acquisition and analysis provide new opportunities for data-driven modelling of the health state of an organism. In this paper, we explore the evolution of temperature patterns generated by telemetry data collected from healthy and infected mice. We investigate several techniques to visualize and identify anomalies in temperature time series as temperature relates to the onset of infectious disease. Visualization tools such as Laplacian Eigenmaps and Multidimensional Scaling allow one to gain an understanding of a dataset as a whole. Anomaly detection tools for nonlinear time series modelling, such as Radial Basis Functions and Multivariate State Estimation Technique, allow one to build models representing a healthy state in individuals. We illustrate these methods on an experimental dataset of 306 Collaborative Cross mice challenged with Salmonella typhimurium and show how interruption in circadian patterns and severity of infection can be revealed directly from these time series within 3 days of the infection event.