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Because most humans resist Mycobacterium tuberculosis infection, there is a paucity of lung samples to study. To address this gap, we infected Diversity Outbred mice with M. tuberculosis and studied the lungs of mice in different disease states. After a low-dose aerosol infection, progressors succumbed to acute, inflammatory lung disease within 60 days, while controllers maintained asymptomatic infection for at least 60 days, and then developed chronic pulmonary tuberculosis (TB) lasting months to more than 1 year. Here, we identified features of asymptomatic M. tuberculosis infection by applying computational and statistical approaches to multimodal data sets. Cytokines and anti-M. tuberculosis cell wall antibodies discriminated progressors vs controllers with chronic pulmonary TB but could not classify mice with asymptomatic infection. However, a novel deep-learning neural network trained on lung granuloma images was able to accurately classify asymptomatically infected lungs vs acute pulmonary TB in progressors vs chronic pulmonary TB in controllers, and discrimination was based on perivascular and peribronchiolar lymphocytes. Because the discriminatory lesion was rich in lymphocytes and CD4 T cell-mediated immunity is required for resistance, we expected CD4 T-cell genes would be elevated in asymptomatic infection. However, the significantly different, highly expressed genes were from B-cell pathways (e.g., Bank1, Cd19, Cd79, Fcmr, Ms4a1, Pax5, and H2-Ob), and CD20+ B cells were enriched in the perivascular and peribronchiolar regions of mice with asymptomatic M. tuberculosis infection. Together, these results indicate that genetically controlled B-cell responses are important for establishing asymptomatic M. tuberculosis lung infection.

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Group B coxsackieviruses (CVBs) cause a wide range of diseases in humans, but no vaccines are currently available to prevent these infections. Previously, we had demonstrated that a live attenuated CVB3 vaccine virus, Mutant 10 (Mt10), offers protection against multiple CVB serotypes as evaluated in various inbred mouse strains; however, the applicability of these findings to the outbred human population remains uncertain. To address this issue, we used Diversity Outbred (DO) mice, whose genome is derived from eight inbred mouse strains that may capture the level of genetic diversity of the outbred human population. To determine the efficacy of the Mt10 vaccine, we established the CVB3 infection model in the DO mice. We noted that CVB3 infection resulted mainly in pancreatitis, although viral RNA was detected in both the pancreas and heart. Histologically, the pancreatic lesions comprised of necrosis, post-necrotic atrophy, and lymphocyte infiltration. In evaluating the efficacy of the Mt10 vaccine, both male and female DO mice were completely protected in challenge studies with CVB3, and viral RNA was not detected in the heart or pancreas. Likewise, vaccine recipients of both sexes showed significant levels of virus-neutralizing antibodies. Furthermore, by using the CVB3 viral protein 1, virus-reactive antibodies were found to be diverse in the order of IgG2c, followed by IgG2a, IgG2b/IgG3, and IgG1. Together, the data suggest that the Mt10 vaccine virus can offer protection against CVB infections that may have translational significance.

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Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies.

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Realizing the clinical promise of cancer immunotherapy is hindered by gaps in our knowledge of in vivo mechanisms underlying treatment response as well as treatment limiting toxicity. Preclinical in vivo model systems and technologies are required to address these knowledge gaps and to surmount the challenges faced in the clinical application of immunotherapy. Mice are commonly used for basic and translational research to support development and testing of immune interventions, including for cancer. Here, we discuss the advantages and the limitations of current models as well as future developments.

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The diet and age of mice can modulate how different genetic variants impact body weight, demonstrating the need to take context into account when performing genetic studies.

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Resolvin E1 (RvE1), a specialized pro-resolving mediator (SPM), improves glucose homeostasis in inbred mouse models of obesity. However, an impediment toward translation is that obesity is a highly heterogenous disease in which individuals will respond very differently to interventions such as RvE1. Thus, there is a need to study SPMs in the context of modeling the heterogeneity of obesity that is observed in humans. We investigated how RvE1 controls the concentration of key circulating metabolic biomarkers using diversity outbred (DO) mice, which mimic human heterogeneity. We first demonstrate that weights of DO mice can be classified into distinct distributions of fat mass (i.e., modeling differing classes of obesity) in response to a high-fat diet and in the human population when examining body composition. Next, we show RvE1 administration based on body weight for four consecutive days after giving mice a high-fat diet led to approximately half of the mice responding positively for serum total gastric inhibitory polypeptide (GIP), glucagon, insulin, glucose, leptin, and resistin. Interestingly, RvE1 improved hyperleptinemia most effectively in the lowest class of fat mass despite adjusting the dose of RvE1 with increasing adiposity. Furthermore, leptin levels after RvE1 treatment were the lowest in those mice that were also RvE1 positive responders for insulin and resistin. Collectively, these results suggest a therapeutic fat mass-dependent window for RvE1, which should be considered in future clinical trials. Moreover, the data underscore the importance of studying SPMs with heterogenous mice as a step toward precision SPM administration in humans.

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BACKGROUND: A strong predictor for the development of alcohol use disorder (AUD) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool to elucidate the genetic basis of behavioral and physiological traits relevant to AUD, but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations make it possible to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification. METHODS: We have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high-density MegaMUGA and GigaMUGA to obtain genotypes ranging from 77,808 to 143,259 SNPs. We also performed RNA sequencing in striatum to map expression QTLs and identify gene expression-trait correlations. We then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exists between DNA sequence, gene expression values, and ethanol-related phenotypes to prioritize our list of positional candidate genes. RESULTS: We observed large amounts of phenotypic variation with the DO population and identified suggestive and significant QTLs associated with ethanol sensitivity on chromosomes 1, 2, and 16. The implicated regions were narrow (4.5-6.9 Mb in size) and each QTL explained ~4-5% of the variance. CONCLUSIONS: Our results can be used to identify alleles that contribute to AUD in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.

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A common step in the analysis of multiparent populations (MPPs) is genotype reconstruction: identifying the founder origin of haplotypes from dense marker data. This process often makes use of a probability model for the pattern of founder alleles along chromosomes, including the relative frequency of founder alleles and the probability of exchanges among them, which depend on a model for meiotic recombination and on the mating design for the population. While the precise experimental design used to generate the population may be used to derive a precise characterization of the model for exchanges among founder alleles, this can be tedious, particularly given the great variety of experimental designs that have been proposed. We describe an approximate model that can be applied for a variety of MPPs. We have implemented the approach in the R/qtl2 software, and we illustrate its use in applications to publicly available data on Diversity Outbred and Collaborative Cross mice.

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Healthspan is a complex trait, influenced by many genes and environmental factors that accelerate or delay aging, reduce or increase disease risk, and extend or reduce lifespan. Thus, assessing the role of genetic variation in aging requires an experimental strategy capable of modeling the genetic and biological complexity of human populations. The goal of the The Jackson Laboratory Nathan Shock Center (JAX NSC) is to provide research resources and training for geroscience investigators that seek to understand the role of genetics and genetic diversity on the fundamental process of aging and diseases of human aging using the laboratory mouse as a model system. The JAX NSC has available novel, deeply characterized populations of aged mice, performs state-of-the-art phenotyping of age-relevant traits, provides systems genetics analysis of complex data sets, and provides all of these resources to the geroscience community. The aged animal resources, phenotyping capacity, and genetic expertise available through the JAX NSC benefit the geroscience community by fostering cutting-edge, novel lines of research that otherwise would not be possible. Over the past 15 years, the JAX NSC has transformed aging research across the geroscience community, providing aging mouse resources and tissues to researchers. All JAX NSC data and tools are publicly disseminated on the Mouse Phenome Database and the JAX NSC website, thus ensuring that the resources generated and expertise acquired through the Center are readily available to the aging research community. The JAX NSC will continue to enhance its ability to perform innovative research using a mammalian model to illuminate novel genotype-phenotype relationships and provide a rational basis for designing effective risk assessments and therapeutic interventions to boost longevity and disease-free healthspan.

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Idiopathic pulmonary fibrosis (IPF) is an incurable genetic disease that affects 5 million people worldwide. The gain-of-function MUC5B promoter variant rs35705950 is the dominant genetic risk factor for IPF, yet has a low penetrance. This raises the possibility that other genes and transcripts affect the penetrance of MUC5B. Previously, we have shown that the concentration of Muc5b in bronchoalveolar epithelia is directly associated with the extent and persistence of bleomycin-induced lung fibrosis in mice. In this study, we investigated whether bleomycin-induced lung injury is Muc5b dependent in genetically divergent strains of mice. Specifically, mice from the eight Diversity Outbred (DO) founders were phenotyped for Muc5b expression and lung fibrosis 3 wk after intratracheal bleomycin administration. Although we identified strains with low Muc5b expression and minimal lung fibrosis (CAST/EiJ and PWK/PhJ) and strains with high Muc5b expression and extensive lung fibrosis (NZO/H1LtJ and WSB/EiJ), there also were strains that did not demonstrate a clear relationship between Muc5b expression and lung fibrosis (129S1/SvlmJ, NOD/ShiLtJ, and C57BL/6J, A/J). Hierarchical clustering suggests that other factors may work in concert with or potentially independent of Muc5b to promote bleomycin-induced lung injury and fibrosis. This study suggests that these strains and their recombinant inbred crosses may prove helpful in identifying the genes and transcripts that interact with Muc5b and cause lung fibrosis.

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BACKGROUND: Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few efforts have been made to model gene expression from histopathology. This study proposes a methodology which predicts selected gene expression values (microarray) from haematoxylin and eosin whole-slide images as an intermediate data modality to identify fulminant-like pulmonary tuberculosis ('supersusceptible') in an experimentally infected cohort of Diversity Outbred mice (n=77). METHODS: Gradient-boosted trees were utilized as a novel feature selector to identify gene transcripts predictive of fulminant-like pulmonary tuberculosis. A novel attention-based multiple instance learning model for regression was used to predict selected genes' expression from whole-slide images. Gene expression predictions were shown to be sufficiently replicated to identify supersusceptible mice using gradient-boosted trees trained on ground truth gene expression data. FINDINGS: The model was accurate, showing high positive correlations with ground truth gene expression on both cross-validation (n = 77, 0.63 ≤ ρ ≤ 0.84) and external testing sets (n = 33, 0.65 ≤ ρ ≤ 0.84). The sensitivity and specificity for gene expression predictions to identify supersusceptible mice (n=77) were 0.88 and 0.95, respectively, and for an external set of mice (n=33) 0.88 and 0.93, respectively. IMPLICATIONS: Our methodology maps histopathology to gene expression with sufficient accuracy to predict a clinical outcome. The proposed methodology exemplifies a computational template for gene expression panels, in which relatively inexpensive and widely available tissue histopathology may be mapped to specific genes' expression to serve as a diagnostic or prognostic tool. FUNDING: National Institutes of Health and American Lung Association.

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BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversity Outbred (DO) mice reflect human-like genetic diversity and develop human-like lung granulomas when infected with Mycobacterium tuberculosis (M.tb) . METHODS: Following M.tb infection, a "supersusceptible" phenotype develops in approximately one-third of DO mice characterized by rapid morbidity and mortality within 8 weeks. These supersusceptible DO mice develop lung granulomas patterns akin to humans. This led us to utilize deep learning to identify supersusceptibility from hematoxylin & eosin (H&E) lung tissue sections utilizing only clinical outcomes (supersusceptible or not-supersusceptible) as labels. FINDINGS: The proposed machine learning model diagnosed supersusceptibility with high accuracy (91.50 ± 4.68%) compared to two expert pathologists using H&E stained lung sections (94.95% and 94.58%). Two non-experts used the imaging biomarker to diagnose supersusceptibility with high accuracy (88.25% and 87.95%) and agreement (96.00%). A board-certified veterinary pathologist (GB) examined the imaging biomarker and determined the model was making diagnostic decisions using a form of granuloma necrosis (karyorrhectic and pyknotic nuclear debris). This was corroborated by one other board-certified veterinary pathologist. Finally, the imaging biomarker was quantified, providing a novel means to convert visual patterns within granulomas to data suitable for statistical analyses. IMPLICATIONS: Overall, our results have translatable implication to improve our understanding of TB and also to the broader field of computational pathology in which clinical outcomes alone can drive automatic identification of interpretable imaging biomarkers, knowledge discovery, and validation of existing clinical biomarkers. FUNDING: National Institutes of Health and American Lung Association.

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Eicosapentaenoic acid (EPA) has garnered attention after the success of the REDUCE-IT trial, which contradicted previous conclusions on EPA for cardiovascular disease risk. Here we first investigated EPA's preventative role on hyperglycemia and hyperinsulinemia. EPA ethyl esters prevented obesity-induced glucose intolerance, hyperinsulinemia, and hyperglycemia in C57BL/6J mice. Supporting NHANES analyses showed that fasting glucose levels of obese adults were inversely related to EPA intake. We next investigated how EPA improved murine hyperinsulinemia and hyperglycemia. EPA overturned the obesity-driven decrement in the concentration of 18-hydroxyeicosapentaenoic acid (18-HEPE) in white adipose tissue and liver. Treatment of obese inbred mice with RvE1, the downstream immunoresolvant metabolite of 18-HEPE, but not 18-HEPE itself, reversed hyperinsulinemia and hyperglycemia through the G-protein coupled receptor ERV1/ChemR23. To translate the findings, we determined if the effects of RvE1 were dependent on host genetics. RvE1's effects on hyperinsulinemia and hyperglycemia were divergent in diversity outbred mice that model human genetic variation. Secondary SNP analyses further confirmed extensive genetic variation in human RvE1/EPA-metabolizing genes. Collectively, the data suggest EPA prevents hyperinsulinemia and hyperglycemia, in part, through RvE1's activation of ERV1/ChemR23 in a host genetic manner. The studies underscore the need for personalized administration of RvE1 based on genetic/metabolic enzyme profiles.

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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.

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Trimethylamine-N-oxide (TMAO), a microbial choline metabolism byproduct that is processed in the liver and excreted into circulation, is associated with increased atherosclerotic lesion formation and cardiovascular disease risk. Genetic regulators of TMAO levels are largely unknown. In the present study, we used 288 mice from a genetically heterogeneous mouse population [Diversity Outbred (DO)] to determine hepatic microRNA associations with TMAO in the context of an atherogenic diet. We also validated findings in two additional animal models of atherosclerosis: liver-specific insulin receptor knockout mice fed a chow diet (LIRKO) and African green monkeys fed high-fat/high-cholesterol diet. Small RNA-sequencing analysis in DO mice, LIRKO mice, and African green monkeys identified only one hepatic microRNA (miR-146a-5p) that is aberrantly expressed across all three models. Moreover, miR-146a-5p levels are associated with circulating TMAO after atherogenic diet in each of these models. We also performed high-resolution genetic mapping and identified a novel quantitative trait locus on Chromosome 12 for TMAO levels. This interval includes two genes, Numb and Dlst, which are inversely correlated with both miR-146a and TMAO and are predicted targets of miR-146a. Both of these genes have been validated as direct targets of miR-146a, though in other cellular contexts. This is the first report to our knowledge of a link between miR-146 and TMAO. Our findings suggest that miR-146-5p, as well as one or more genes at the Chromosome 12 QTL (possibly Numb or Dlst), is strongly linked to TMAO levels and likely involved in the control of atherosclerosis.

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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.

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Chronically altered levels of circulating lipids, termed dyslipidemia, is a significant risk factor for a number of metabolic and cardiovascular morbidities. MicroRNAs (miRNAs) have emerged as important regulators of lipid balance, have been implicated in dyslipidemia, and have been proposed as candidate therapeutic targets in lipid-related disorders including atherosclerosis. A major limitation of most murine studies of miRNAs in lipid metabolic disorders is that they have been performed in just one (or very few) inbred strains, such as C57BL/6. Moreover, although individual miRNAs have been associated with lipid phenotypes, it is well understood that miRNAs likely work together in functional modules. To address these limitations, we implemented a systems genetics strategy using the Diversity Outbred (DO) mouse population. Specifically, we performed gene and miRNA expression profiling in the livers from ~300 genetically distinct DO mice after 18 wk on either a high-fat/high-cholesterol diet or a high-protein diet. Large-scale correlative analysis of these data with a wide range of cardio-metabolic end points revealed a co-regulated module of miRNAs significantly associated with circulating low-density lipoprotein cholesterol (LDL-C) levels. The hubs of this module were identified as miR-199a, miR-181b, miR-27a, miR-21_-_1, and miR-24. In sum, we demonstrate that a high-fat/high-cholesterol diet robustly rewires the miRNA regulatory network, and we identify a small group of co-regulated miRNAs that may exert coordinated effects to control circulating LDL-C.

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Massively parallel RNA sequencing (RNA-seq) has yielded a wealth of new insights into transcriptional regulation. A first step in the analysis of RNA-seq data is the alignment of short sequence reads to a common reference genome or transcriptome. Genetic variants that distinguish individual genomes from the reference sequence can cause reads to be misaligned, resulting in biased estimates of transcript abundance. Fine-tuning of read alignment algorithms does not correct this problem. We have developed Seqnature software to construct individualized diploid genomes and transcriptomes for multiparent populations and have implemented a complete analysis pipeline that incorporates other existing software tools. We demonstrate in simulated and real data sets that alignment to individualized transcriptomes increases read mapping accuracy, improves estimation of transcript abundance, and enables the direct estimation of allele-specific expression. Moreover, when applied to expression QTL mapping we find that our individualized alignment strategy corrects false-positive linkage signals and unmasks hidden associations. We recommend the use of individualized diploid genomes over reference sequence alignment for all applications of high-throughput sequencing technology in genetically diverse populations.

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