Genome Wide Identification of SARS-CoV Susceptibility Loci Using the Collaborative Cross

Abstract

New systems genetics approaches are needed to rapidly identify host genes and genetic networks that regulate complex disease outcomes. Using genetically diverse animals from incipient lines of the Collaborative Cross mouse panel, we demonstrate a greatly expanded range of phenotypes relative to classical mouse models of SARS-CoV infection including lung pathology, weight loss and viral titer. Genetic mapping revealed several loci contributing to differential disease responses, including an 8.5Mb locus associated with vascular cuffing on chromosome 3 that contained 23 genes and 13 noncoding RNAs. Integrating phenotypic and genetic data narrowed this region to a single gene, Trim55, an E3 ubiquitin ligase with a role in muscle fiber maintenance. Lung pathology and transcriptomic data from mice genetically deficient in Trim55 were used to validate its role in SARS-CoV-induced vascular cuffing and inflammation. These data establish the Collaborative Cross platform as a powerful genetic resource for uncovering genetic contributions of complex traits in microbial disease severity, inflammation and virus replication in models of outbred populations.

Allelic Variation in the Toll-Like Receptor Adaptor ProteinTicam2Contributes to SARS-Coronavirus Pathogenesis in Mice

“…In contrast to previous genetic mapping studies using the entire CC population (Ferris et al 2013; Gralinski et al 2015), we focused on two strains exhibiting extreme responses to SARS-CoV infection, predicting that they would have multiple QTL driving these extreme responses. Further, we hypothesized that each of those QTL would contrast alleles from unique pairs of founder haplotypes (as these susceptibility responses are outside the range of responses seen in other inbred strains).…”

Mouse Models as Resources for Studying Infectious Diseases

“…75,79 (D) Alternatively, investigators can take advantage of the phenotypic variation in the CC to perform genetic mapping studies to identify and study polymorphic host genes associated with variation in infection outcomes. 71,73,74 .…”

“…71,73,77e79 These results indicate that by taking advantage of the genetic diversity present in the CC, investigators can develop novel models of pathogen-induced disease that more closely represent disease phenotypes observed in human populations. Furthermore, once mouse strains with novel disease phenotypes have been identified, it is possible to identify the genetic loci responsible for these novel disease outcomes 71,73 and gain additional insights into the mechanisms underlying disease pathogenesis.…”

Giving the Genes a Shuffle: Using Natural Variation to Understand Host Genetic Contributions to Viral Infections

“…Candidate genes for various phenotypes, such as susceptibility to Aspergillus fumigatus infections [33], energy balance traits [34], differences in hematological parameters [35], susceptibility to Klebsiella pneumoniae [36], and neutrophilic inflammation due to house dust mite-induced asthma [37], were successfully identified. Moreover, mapping efforts revealed expression QTLs for extreme host responses to influenza A virus (IAV) infections [38], host response QTLs to IAV [39], and severe acute respiratory syndrome coronavirus (SARS-CoV) pathogenesis [40]. A common observation in all of the studies was that pre-CC lines exhibit an enhanced phenotypic range compared with the variation observed in the eight founder strains or other classical inbred strains and that it was possible to dissect traits that were thought to be inseparably entwined.…”

“…This is important as it has been shown that novel disease phenotypes might be discovered in the CC (severe neuroinvasive disease and chronic WNV infection [44]). New models for diseases that are completely unrelated (spontaneous colitis [41], human leukocyte adhesion and recruitment deficiencies [55]) or related to the study design (SARS-CoV [40], Ebola [42]) will generate a panel of variable disease-state mouse models that capture the different phases of disease seen in human populations. After all phenotype measurements are obtained, genetic mapping is utilized to calculate the likelihood of every position in the genome being associated with the analyzed phenotype, and regions with the highest logarithm of the odds (LOD) score are identified as QTLs.…”