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In mammals the regulation of genomic instability plays a key role in tumor suppression and also controls genome plasticity, which is important for recombination during the processes of immunity and meiosis. Most studies to identify regulators of genomic instability have been performed in cells in culture or in systems that report on gross rearrangements of the genome, yet subtle differences in the level of genomic instability can contribute to whole organism phenotypes such as tumor predisposition. Here we performed a genome-wide association study in a population of 1379 outbred Crl:CFW(SW)-US_P08 mice to dissect the genetic landscape of micronucleus formation, a biomarker of chromosomal breaks, whole chromosome loss, and extranuclear DNA. Variation in micronucleus levels is a complex trait with a genome-wide heritability of 53.1%. We identify seven loci influencing micronucleus formation (false discovery rate <5%), and define candidate genes at each locus. Intriguingly at several loci we find evidence for sexual dimorphism in micronucleus formation, with a locus on chromosome 11 being specific to males.

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely-used R/qtl software package to include multiparental populations, better handles modern high-dimensional data... R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely used R/qtl software package to include multiparent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes, including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2, which is free and open source software written in the R and C++ programming languages, comes with a test framework.

CD8 T cells comprising the memory pool display considerable heterogeneity, with individual cells differing in phenotype and function. This review will focus on our current understanding of heterogeneity within the antigen-specific memory CD8 T cell compartment and classifications of memory CD8 T cell subsets with defined and discrete functionalities. Recent data suggest that phenotype and/or function of numerically stable circulatory memory CD8 T cells are defined by the age of memory CD8 T cell (or time after initial antigen-encounter). In addition, history of antigen stimulations has a profound effect on memory CD8 T cell populations, suggesting that repeated infections (or vaccination) have the capacity to further shape the memory CD8 T cell pool. Finally, genetic background of hosts and history of exposure to diverse microorganisms likely contribute to the observed heterogeneity in the memory CD8 T cell compartment. Extending our tool box and exploring alternative mouse models (i.e., \"dirty\" and/or outbred mice) to encompass and better model diversity observed in humans will remain an important goal for the near future that will likely shed new light into the mechanisms that govern biology of memory CD8 T cells.

Induction of demyelination in the central nervous system (CNS) of experimental mice using cuprizone is widely used as an animal model for studying the pathogenesis and treatment of demyelination. However, different mouse strains used result in different pathological outcomes. Moreover, because current medicinal treatments are not always effective in multiple sclerosis patients, so the study of exogenous cell transplantation in an animal model is of great importance. The aims of the present study were to establish an alternative ICR outbred mouse model for studying demyelination and to evaluate the effects of intravenous cell transplantation in the present developed mouse model. Two sets of experiments were conducted. Firstly, ICR outbred and BALB/c inbred mice were fed with 0.2% cuprizone for 6 consecutive weeks; then demyelinating scores determined by luxol fast blue stain or immunolabeling with CNPase were evaluated. Secondly, attenuation of demyelination in ICR mice by intravenous injection of mES cells was studied. Scores for demyelination in the brains of ICR mice receiving cell injection (mES cells-injected group) and vehicle (sham-inoculated group) were assessed and compared. The results showed that cuprizone significantly induced demyelination in the cerebral cortex and corpus callosum of both ICR and BALB/c mice. Additionally, intravenous transplantation of mES cells potentially attenuated demyelination in ICR mice compared with sham-inoculated groups. The present study is among the earliest reports to describe the cuprizone-induced demyelination in ICR outbred mice. Although it remains unclear whether mES cells or trophic effects from mES cells are the cause of enhanced remyelination, the results of the present study may shed some light on exogenous cell therapy in central nervous system demyelinating diseases.

Abstract Study Objectives This study describes high-throughput phenotyping strategies for sleep and circadian behavior in mice, including examinations of robustness, reliability, and heritability among Diversity Outbred (DO) mice and their eight founder strains. Methods We performed high-throughput sleep and circadian phenotyping in male mice from the DO population (n = 338) and their eight founder strains: A/J (n = 6), C57BL/6J (n = 14), 129S1/SvlmJ (n = 6), NOD/LtJ (n = 6), NZO/H1LtJ (n = 6), CAST/EiJ (n = 8), PWK/PhJ (n = 8), and WSB/EiJ (n = 6). Using infrared beam break systems, we defined sleep as at least 40 s of continuous inactivity and quantified sleep–wake amounts and bout characteristics. We developed assays to measure sleep latency in a new environment and during a modified Murine Multiple Sleep Latency Test, and estimated circadian period from wheel-running experiments. For each trait, broad-sense heritability (proportion of variability explained by all genetic factors) was derived in founder strains, while narrow-sense heritability (proportion of variability explained by additive genetic effects) was calculated in DO mice. Results Phenotypes were robust to different inactivity durations to define sleep. Differences across founder strains and moderate/high broad-sense heritability were observed for most traits. There was large phenotypic variability among DO mice, and phenotypes were reliable, although estimates of heritability were lower than in founder mice. This likely reflects important nonadditive genetic effects. Conclusions A high-throughput phenotyping strategy in mice, based primarily on monitoring of activity patterns, provides reliable and heritable estimates of sleep and circadian traits. This approach is suitable for discovery analyses in DO mice, where genetic factors explain some proportion of phenotypic variation.

Outbred mice (ICR) with different genotypes and phenotypes have been reported to be more suitable for scientific testing than inbred mice because they are more similar to humans. To investigate whether the sex and genetic background of the mice are important factors in the development of hyperglycemia, we used ICR mice and divided them into male, female, and ovariectomized female (FOVX) groups and treated them with streptozotocin (STZ) for five consecutive days to induce diabetes. Our results show that fasting blood glucose and hemoglobin A1c (HbA1c) levels were significantly higher in diabetes-induced males (M-DM) and ovariectomized diabetes-induced females (FOVX-DM) than in diabetes-induced females (F-DM) at 3 and 6 weeks after STZ treatment. Furthermore, the M-DM group showed the most severe glucose tolerance, followed by the FOVX-DM and F-DM groups, suggesting that ovariectomy affects glucose tolerance in female mice. The size of pancreatic islets in the M-DM and FOVX-DM groups was significantly different from that of the F-DM group. The M-DM and FOVX-DM groups had pancreatic beta-cell dysfunction 6 weeks after STZ treatment. Urocortin 3 and somatostatin inhibited insulin secretion in the M-DM and FOVX-DM groups. Overall, our results suggest that glucose metabolism in mice is dependent on sex and/or genetic background.

It is essential to elucidate the molecular mechanisms underlying liver transplant tolerance and rejection. In cases of mouse liver transplantation between inbred strains, immunological rejection of the allograft is reduced with spontaneous apoptosis without immunosuppressive drugs, which differs from the actual clinical result. This may be because inbred strains are genetically homogeneous and less heterogeneous than others. We exploited outbred CD1 mice, which show highly heterogeneous genotypes among individuals, to search for biomarkers related to immune responses and to construct a model for predicting the outcome of liver allografting. Of the 36 mice examined, 18 died within 3 weeks after transplantation, while the others survived for more than 6 weeks. Whole-exome sequencing of the 36 donors revealed more than 9 million variants relative to the C57BL/6 J reference. We selected 6517 single-nucleotide and indel variants and performed machine learning to determine whether or not we could predict the prognosis of each genotype. Models were built by both deep learning with a one-dimensional convolutional neural network and linear classification and evaluated by leave-one-out cross-validation. Given that one short-lived mouse died early in an accident, the models perfectly predicted the outcome of all individuals, suggesting the importance of genotype collection. In addition, linear classification models provided a list of loci potentially responsible for these responses. The present methods as well as results is likely to be applicable to liver transplantation in humans.

Recent developments allowed generating multiple high-quality ‘omics’ data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

In this study, Tyler et al. analyzed the complex genetic architecture of metabolic disease-related traits using the Diversity Outbred mouse population Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. The challenge of reducing these data to specific hypotheses has become increasingly acute with the advent of genome-scale data resources. Multi-parent populations derived from model organisms provide a resource for developing methods to understand this complexity. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Transcript data were reduced to functional gene modules with weighted gene coexpression network analysis (WGCNA), which were used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects and alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.

Hypervirulent Klebsiella pneumoniae (hvKp) is of increasing concern because it can infect individuals in community and health care settings and because such infections are becoming difficult to treat. Identification of hvKp is important for patient care and to track its global spread. The genetic definition of hvKp, which can be used for its identification and the development of diagnostic tests, has not been optimized. Determination of possession of 4 of 5 genes that are present on the hvKp-specific virulence plasmid is highly accurate for identifying hvKp. However, an ongoing issue is whether strains that possess only some of these markers are still hypervirulent. The Galleria mellonella model and, less commonly, the murine infection model have been used to assess the virulence of these ambiguously identifiable strains. This report demonstrates that the murine model but not the G. mellonella model accurately identifies suspected hvKp strains. This information is critical for the development of diagnostics for patient care and for future research studies. Hypervirulent Klebsiella pneumoniae (hvKp) is an emerging pathogen of increasing concern due to its ability to cause serious organ and life-threatening infections in healthy individuals and its increasing acquisition of antimicrobial resistance determinants. Identification of hvKp is critical for patient care and epidemiologic and research studies. Five genotypic markers on the hvKp-specific virulence plasmid can accurately differentiate hvKp from the less virulent classical K. pneumoniae (cKp) strain, but it is unclear whether the possession of fewer markers accurately predicts the hvKp pathotype. Likewise, the effect, if any, of various antimicrobial resistance factors on the pathogenic potential of hvKp has been incompletely explored. The Galleria mellonella infection model is often used to assess virulence, but this tool has not been validated. Therefore, levels of lethality of defined hvKp and cKp strain cohorts were compared in Galleria and outbred mouse models. The murine model, but not the G. mellonella model, accurately differentiated hvKp from cKp strains. Therefore, isolates in which the pathogenic potential is ambiguous due to an incomplete hvKp biomarker profile, an incomplete pLVPK-like hvKp-specific virulence plasmid, antimicrobial resistance that could decrease biofitness, and/or the lack of a characteristic clinical presentation should be validated in an outbred murine model. These data will assist in determining the minimal genomic content needed for full expression of the hypervirulence phenotype. This information, in turn, is critical for the development of the pragmatic point-of-care testing requisite for patient care and for the performance of epidemiologic and research studies going forward. IMPORTANCE Hypervirulent Klebsiella pneumoniae (hvKp) is of increasing concern because it can infect individuals in community and health care settings and because such infections are becoming difficult to treat. Identification of hvKp is important for patient care and to track its global spread. The genetic definition of hvKp, which can be used for its identification and the development of diagnostic tests, has not been optimized. Determination of possession of 4 of 5 genes that are present on the hvKp-specific virulence plasmid is highly accurate for identifying hvKp. However, an ongoing issue is whether strains that possess only some of these markers are still hypervirulent. The Galleria mellonella model and, less commonly, the murine infection model have been used to assess the virulence of these ambiguously identifiable strains. This report demonstrates that the murine model but not the G. mellonella model accurately identifies suspected hvKp strains. This information is critical for the development of diagnostics for patient care and for future research studies.