Laboratory mice, while paramount for understanding basic biological phenomena, are limited in modeling complex diseases of humans and other free-living mammals. Because the microbiome is a major factor in mammalian physiology, we aimed to identify a naturally evolved reference microbiome to better recapitulate physiological phenomena relevant in the natural world outside the laboratory. Among 21 distinct mouse populations worldwide, we identified a closely related wild relative to standard laboratory mouse strains. Its bacterial gut microbiome differed significantly from its laboratory mouse counterpart and was transferred to and maintained in laboratory mice over several generations. Laboratory mice reconstituted with natural microbiota exhibited reduced inflammation and increased survival following influenza virus infection and improved resistance against mutagen/inflammation-induced colorectal tumorigenesis. By demonstrating the host fitness-promoting traits of natural microbiota, our findings should enable the discovery of protective mechanisms relevant in the natural world and improve the modeling of complex diseases of free-living mammals. VIDEO ABSTRACT.

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The genome of the laboratory mouse is thought to be a mosaic of regions with distinct subspecific origins. We have developed a high-resolution map of the origin of the laboratory mouse by generating 25,400 phylogenetic trees in 100 kb intervals spanning the genome. On average 92% of the genome is of M. m. domesticus origin and the distribution of diversity is strikingly non random among the chromosomes. There are large regions of extremely low diversity, representing blind spots for studies of natural variation and complex traits, as well as hot spots of diversity. In contrast with the mosaic model we found that the majority of the genome has intermediate levels of variation of intrasubspecific origin. Finally, the wild-derived mouse strains that are supposed to represent different mouse subspecies show substantial intersubspecific introgression. This has serious implications for evolutionary studies that assume these are pure representatives of a given subspecies.Laboratory mice, the most popular model organism in mammalian genetics 1,2 , were derived from wild mice belonging to the Mus musculus species by an intricate process that included the generation of "fancy" mice in both Asia and Europe and a complex web of relationships among inbred strains 3 . Early studies demonstrated that the mitochondria and the Y chromosome present in many classical laboratory strains were derived from different subspecies, M. m. domesticus for the mitochondria and M. m. musculus for the Y chromosome 4,5 . Furthermore, the Y chromosome was introduced in the laboratory mouse through M. m. molossinus males 6 . Based on these findings, it was proposed that the genomes of inbred strains were a mosaic of regions with different subspecific origin 7 . Recently, the fine structure of such mosaic variation has been described 8 . This study reported that strain-to-strain comparisons revealed regions with extremely high variation spanning one third of the genome and regions with extremely low variation covering the remaining two thirds of the genome. This distinctively bimodal distribution was assumed to represent regions with the same and different subspecific origin. This mosaic model has been the driving concept behind mouse association mapping studies and haplotype analysis [9][10][11][12] . However, the origin of a given region of a laboratory strain could not be directly assigned to a subspecies due to the lack of reference sequences for the three major mouse subspecies. Subsequent studies raised questions regarding the haplotype structure 11,13 , the effect of ascertainment biases in subspecific assignment [14][15][16] and the contributions of intersubspecific

Genetic variation contributes to host responses and outcomes following infection by influenza A virus or other viral infections. Yet narrow windows of disease symptoms and confounding environmental factors have made it difficult to identify polymorphic genes that contribute to differential disease outcomes in human populations. Therefore, to control for these confounding environmental variables in a system that models the levels of genetic diversity found in outbred populations such as humans, we used incipient lines of the highly genetically diverse Collaborative Cross (CC) recombinant inbred (RI) panel (the pre-CC population) to study how genetic variation impacts influenza associated disease across a genetically diverse population. A wide range of variation in influenza disease related phenotypes including virus replication, virus-induced inflammation, and weight loss was observed. Many of the disease associated phenotypes were correlated, with viral replication and virus-induced inflammation being predictors of virus-induced weight loss. Despite these correlations, pre-CC mice with unique and novel disease phenotype combinations were observed. We also identified sets of transcripts (modules) that were correlated with aspects of disease. In order to identify how host genetic polymorphisms contribute to the observed variation in disease, we conducted quantitative trait loci (QTL) mapping. We identified several QTL contributing to specific aspects of the host response including virus-induced weight loss, titer, pulmonary edema, neutrophil recruitment to the airways, and transcriptional expression. Existing whole-genome sequence data was applied to identify high priority candidate genes within QTL regions. A key host response QTL was located at the site of the known anti-influenza Mx1 gene. We sequenced the coding regions of Mx1 in the eight CC founder strains, and identified a novel Mx1 allele that showed reduced ability to inhibit viral replication, while maintaining protection from weight loss.

Mouse genetic resources include inbred strains, recombinant inbred lines, chromosome substitution strains, heterogeneous stocks, and the Collaborative Cross (CC). These resources were generated through various breeding designs that potentially produce different genetic architectures, including the level of diversity represented, the spatial distribution of the variation, and the allele frequencies within the resource. By combining sequencing data for 16 inbred strains and the recorded history of related strains, the architecture of genetic variation in mouse resources was determined. The most commonly used resources harbor only a fraction of the genetic diversity of Mus musculus, which is not uniformly distributed thus resulting in many blind spots. Only resources that include wild-derived inbred strains from subspecies other than M. m. domesticus have no blind spots and a uniform distribution of the variation. Unlike other resources that are primarily suited for gene discovery, the CC is the only resource that can support genome-wide network analysis, which is the foundation of systems genetics. The CC captures significantly more genetic diversity with no blind spots and has a more uniform distribution of the variation than all other resources. Furthermore, the distribution of allele frequencies in the CC resembles that seen in natural populations like humans in which many variants are found at low frequencies and only a minority of variants are common. We conclude that the CC represents a dramatic improvement over existing genetic resources for mammalian systems biology applications.