Collaborative Cross and Diversity Outbred data resources in the Mouse Phenome Database

Genetic diversity shapes behavioral outcomes and reveals sex differences in mice exposed to early life stress

Understanding how genetic variability shapes responses to environmental and developmental factors is critical for advancing translational neuroscience. However, most preclinical studies rely on inbred mouse strains that do not capture the genetic complexity of human populations. One key area of translational research focuses on identifying the neural and behavioral consequences of early life trauma. Rodent models of childhood neglect, such as maternal separation with early weaning (MSEW), have been used in isogenic mouse strains like C57BL/6J (B6) to identify behavioral domains and neural loci of deficits stemming from exposure to MSEW. To understand how genetic diversity may contribute to the outcomes produced by MSEW, and thus inform future studies on the topic, we utilized the Jackson Laboratory Diversity Outbred (DO) line, a population derived from eight founder strains that exhibit broad genetic and phenotypic heterogeneity. We first compared MSEW effects on social behavior in DO mice versus B6 mice, because we have previously found social behavior deficits in B6 mice with a history of MSEW. Indeed, we established that MSEW incited social motivation deficits in DO mice, in a sex-specific manner. We then expanded our investigation of DO mice to test MSEW-related changes in anxiety-like behavior, fear learning and expression, and reward-seeking. Results revealed that MSEW produces distinct, sex-specific phenotypes: female DO mice displayed reduced social motivation and elevated anxiety-like behavior, while male DO mice showed attenuated CS-evoked fear expression and diminished reward-seeking behavior. Additionally, immunohistochemical analysis revealed increased Fos expression in the paraventricular nucleus of the hypothalamus (PVN) in MSEW-exposed DO mice, both at baseline and following acute stress. These findings highlight the importance of incorporating genetically diverse models to better capture the nuances of early life adversity-related outcomes relevant to human populations.

Effects of Aging on Glucose and Lipid Metabolism in Mice

Aging is accompanied by multiple molecular changes that contribute to aging associated pathologies, such as accumulation of cellular damage and mitochondrial dysfunction. Tissue metabolism can also change with age, in part, because mitochondria are central to cellular metabolism. Moreover, the cofactor NAD+, which is reported to decline across multiple tissues during aging, plays a central role in metabolic pathways such as glycolysis, the tricarboxylic acid cycle, and the oxidative synthesis of nucleotides, amino acids, and lipids. To further characterize how tissue metabolism changes with age, we intravenously infused [U-13C]-glucose into young and old C57BL/6J, WSB/EiJ, and diversity outbred mice to trace glucose fate into downstream metabolites within plasma, liver, gastrocnemius muscle, and brain tissues. We found that glucose incorporation into central carbon and amino acid metabolism was robust during healthy aging across these different strains of mice. We also observed that levels of NAD+, NADH, and the NAD+/NADH ratio were unchanged in these tissues with healthy aging. However, aging tissues, particularly brain, exhibited evidence of upregulated fatty acid and sphingolipid metabolism reactions that regenerate NAD+ from NADH. These data suggest that NAD+-generating lipid metabolism reactions may help to maintain the NAD+/NADH ratio during healthy aging.

Red blood cell urate levels are linked to hemolysis in vitro and post-transfusion as a function of donor sex, population and genetic polymorphisms in SLC2A9 and ABCG2

BACKGROUND

Storage of packed red blood cells (RBCs) for transfusion leads to biochemical and morphological changes, increasing hemolysis risk. Urate levels in blood bags at donation contribute to the molecular heterogeneity and hemolytic propensity of stored RBCs. However, studies to date have been underpowered to investigate at scale the contribution of donor demographics and genetics to the heterogeneity in urate levels across donations.

STUDY DESIGN AND METHODS

Urate levels were measured in 13,091 RBC units from the REDS study. Characteristics tested included hemolysis parameters (spontaneous, osmotic, oxidative) at storage end and post-transfusion hemoglobin (Hb) increments in recipients. Donor demographics, urate levels, and genetic variants were analyzed for associations with these outcomes.

RESULTS

Elevated urate levels were linked to male sex, older age, high BMI, and Asian descent. Units with high urate levels exhibited increased spontaneous and osmotic hemolysis, while oxidative hemolysis was unaffected. Genetic variants in SLC2A9 (V282I) and ABCG2 (Q141K) were strongly associated with elevated urate, particularly in Asian donors. Post-transfusion analyses revealed that units from female donors carrying these variants were associated with reduced Hb increments, with up to a 31% reduction in efficacy. This effect was not observed in male donors.

DISCUSSION

RBC urate levels and genetic traits significantly impact storage quality and transfusion outcomes. These findings highlight the importance of donor molecular characteristics for optimizing transfusion strategies. Moreover, genetic and metabolic insights may inform donor recruitment efforts, providing health feedback to volunteers while ensuring effective transfusion products.

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

Phenotype variability in diet-induced obesity and response to (-)-epigallocatechin gallate supplementation in a Diversity Outbred mouse cohort: A model for exploring gene x diet interactions for dietary bioactives

The flavan-3-ol (-)-epigallocatechin gallate (EGCG) blunts obesity in inbred mice, but human clinical trials have yielded mixed results. Genetic homogeneity in preclinical models may explain translational disconnect between rodents and humans. The Diversity Outbred (DO) mouse model provides genotype and phenotype variability for characterization of gene x environment (i.e., diet) interactions. We conducted a longitudinal phenotyping study in DO mice. Mice (n = 50) were fed a high-fat diet for 8 weeks and then a high-fat diet + 0.3% EGCG for 8 weeks. We hypothesized that obesity and any protective effects of EGCG would exhibit extreme variability in these genetically heterogeneous mice. As anticipated, DO mice exhibited extreme variation in body composition at baseline (4%-13.9% fat), after 8 weeks of high-fat diet (6.5%-38.1% fat), and after 8 weeks of high-fat diet + EGCG (7.6%-42.6% fat), greater than what is observed in inbred mice. All 50 mice gained body fat on the high-fat diet (changes from baseline of +5% ± 640%). Intriguingly, adiposity variability increased when EGCG was added to the diet (changes from the high-fat diet alone of -52% ± 390%), with 11/50 mice losing body fat. We postulate that the explanation for this variability is genetic heterogeneity. Our data confirm the promise for EGCG to manage obesity but suggest that genetic factors may exert significant control over the efficacy of EGCG. Larger studies in DO mice are needed for quantitative trait loci mapping to identify genetic loci governing EGCG x obesity interactions and translate these findings to precision nutrition in humans.

A hematology-based clock derived from the Study of Longitudinal Aging in Mice to estimate biological age

Biological clocks and other molecular biomarkers of aging are difficult to implement widely in a clinical setting. In this study, we used routinely collected hematological markers to develop an aging clock to predict blood age and determine whether the difference between predicted age and chronologic age (aging gap) is associated with advanced aging in mice. Data from 2,562 mice of both sexes and three strains were drawn from two longitudinal studies of aging. Eight hematological variables and two metabolic indices were collected longitudinally (12,010 observations). Blood age was predicted using a deep neural network. Blood age was significantly correlated with chronological age, and aging gap was positively associated with mortality risk and frailty. Platelets were identified as the strongest age predictor by the deep neural network. An aging clock based on routinely collected blood measures has the potential to provide a practical clinical tool to better understand individual variability in the aging process.

Lessons from Klotho mouse models to understand mineral homeostasis

AIM

Klotho, a key component of the endocrine fibroblast growth factor receptor-fibroblast growth factor axis, is a multi-functional protein that impacts renal electrolyte handling. The physiological significance of Klotho will be highlighted in the regulation of calcium, phosphate, and potassium metabolism.

METHODS

In this review, we compare several murine models with different renal targeted deletions of Klotho and the insights into the molecular and physiological function that these models offer.

RESULTS

In vivo, Klotho deficiency is associated with severely impaired mineral metabolism, with consequences on growth, longevity and disease development. Additionally, we explore the perspectives of Klotho in renal pathology and vascular events, as well as potential Klotho treatment options.

CONCLUSION

This comprehensive review emphasizes the use of Klotho to shed light on deciphering the renal molecular in vivo mechanisms in electrolyte handling, as well as novel therapeutic interventions.

Gene-environment interactions within a precision environmental health framework

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Cell type-specific network analysis in Diversity Outbred mice identifies genes potentially responsible for human bone mineral density GWAS associations

Genome-wide association studies (GWASs) have identified many sources of genetic variation associated with bone mineral density (BMD), a clinical predictor of fracture risk and osteoporosis. Aside from the identification of causal genes, other difficult challenges to informing GWAS include characterizing the roles of predicted causal genes in disease and providing additional functional context, such as the cell type predictions or biological pathways in which causal genes operate. Leveraging single-cell transcriptomics (scRNA-seq) can assist in informing BMD GWAS by linking disease-associated variants to genes and providing a cell type context for which these causal genes drive disease. Here, we use large-scale scRNA-seq data from bone marrow-derived stromal cells cultured under osteogenic conditions (BMSC-OBs) from Diversity Outbred (DO) mice to generate cell type-specific networks and contextualize BMD GWAS-implicated genes. Using trajectories inferred from the scRNA-seq data, we identify networks enriched with genes that exhibit the most dynamic changes in expression across trajectories. We discover 21 network driver genes, which are likely to be causal for human BMD GWAS associations that colocalize with expression/splicing quantitative trait loci (eQTL/sQTL). These driver genes, including Fgfrl1 and Tpx2, along with their associated networks, are predicted to be novel regulators of BMD via their roles in the differentiation of mesenchymal lineage cells. In this work, we showcase the use of single-cell transcriptomics from mouse bone-relevant cells to inform human BMD GWAS and prioritize genetic targets with potential causal roles in the development of osteoporosis.

Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.

Alternating Arenavirus Vector Immunization Generates Robust Polyfunctional Genotype Cross-Reactive Hepatitis B Virus-Specific CD8 T-Cell Responses and High Anti-Hepatitis B Surface Antigen Titers

Hepatitis B Virus (HBV) is a major driver of infectious disease mortality. Curative therapies are needed and ideally should induce CD8 T cell-mediated clearance of infected hepatocytes plus anti-hepatitis B surface antigen (HBsAg) antibodies (anti-HBs) to neutralize residual virus. We developed a novel therapeutic vaccine using non-replicating arenavirus vectors. Antigens were screened for genotype conservation and magnitude and genotype reactivity of T cell response, then cloned into Pichinde virus (PICV) vectors (recombinant PICV, GS-2829) and lymphocytic choriomeningitis virus (LCMV) vectors (replication-incompetent, GS-6779). Alternating immunizations with GS-2829 and GS-6779 induced high-magnitude HBV T cell responses, and high anti-HBs titers. Dose schedule optimization in macaques achieved strong polyfunctional CD8 T cell responses against core, HBsAg, and polymerase and high titer anti-HBs. In AAV-HBV mice, GS-2829 and GS-6779 were efficacious in animals with low pre-treatment serum HBsAg. Based on these results, GS-2829 and GS-6779 could become a central component of cure regimens.

The crucial role of diverse animal models to investigate cochlear aging and hearing loss

Age-related hearing loss affects a large and growing segment of the population, with profound impacts on quality of life. Age-related pathology of the cochlea-the mammalian hearing organ-underlies age-related hearing loss. Because investigating age-related changes in the cochlea in humans is challenging and often impossible, animal models are indispensable to investigate these mechanisms as well as the complex consequences of age-related hearing loss on the brain and behavior. In this review, we advocate for a comparative and interdisciplinary approach while also addressing the challenges of comparing age-related hearing loss across species with varying lifespans. We describe the experimental advantages and limitations as well as areas for future research in well-established models of age-related hearing loss, including mice, rats, gerbils, chinchillas, and birds. We also indicate the need to expand characterization of age-related hearing loss in other established animal models, especially guinea pigs, cats, and non-human primates, in which auditory function is well characterized but age-related cochlear pathology is understudied. Finally, we highlight the potential of emerging animal models for advancing our understanding of age-related hearing loss, including deer mice, with their notably extended lifespans and preserved hearing, naked mole rats, with their exceptional longevity and extensive vocal communications, as well as zebrafish, which offer genetic tractability and suitability for drug screening. Ultimately, a comparative and interdisciplinary approach in auditory research, combining insights from various animal models with human studies, is key to robust and reliable research outcomes that better advance our understanding and treatment of age-related hearing loss.

Genetic diversity drives extreme responses to traumatic brain injury and post-traumatic epilepsy

Traumatic brain injury (TBI) is a complex and heterogeneous condition that can cause wide-spectral neurological sequelae such as behavioral deficits, sleep abnormalities, and post-traumatic epilepsy (PTE). However, understanding the interaction of TBI phenome is challenging because few animal models can recapitulate the heterogeneity of TBI outcomes. We leveraged the genetically diverse recombinant inbred Collaborative Cross (CC) mice panel and systematically characterized TBI-related outcomes in males from 12 strains of CC and the reference C57BL/6J mice. We identified unprecedented extreme responses in multiple clinically relevant traits across CC strains, including weight change, mortality, locomotor activity, cognition, and sleep. Notably, we identified CC031 mouse strain as the first rodent model of PTE that exhibit frequent and progressive post-traumatic seizures after moderate TBI induced by lateral fluid percussion. Multivariate analysis pinpointed novel biological interactions and three principal components across TBI-related modalities. Estimate of the proportion of TBI phenotypic variability attributable to strain revealed large range of heritability, including >70% heritability of open arm entry time of elevated plus maze. Our work provides novel resources and models that can facilitate genetic mapping and the understanding of the pathobiology of TBI and PTE.

Human papillomaviruses: Knowns, mysteries, and unchartered territories

There has been an explosion in the number of papillomaviruses that have been identified and fully sequenced. Yet only a minute fraction of these has been studied in any detail. Most of our molecular research efforts have focused on the E6 and E7 proteins of "high-risk," cancer-associated human papillomaviruses (HPVs). Interactions of the high-risk HPV E6 and E7 proteins with their respective cellular targets, the p53 and the retinoblastoma tumor suppressors, have been investigated in minute detail. Some have thus questioned if research on papillomaviruses remains an exciting and worthwhile area of investigation. However, fundamentally new insights on the biological activities and cellular targets of the high-risk HPV E6 and E7 proteins have been discovered and previously unstudied HPVs have been newly associated with human diseases. HPV infections continue to be an important cause of human morbidity and mortality and since there are no antivirals to combat HPV infections, research on HPVs should remain attractive to new investigators and biomedical funding agencies, alike.

Single-Cell Transcriptomics of Bone Marrow Stromal Cells in Diversity Outbred Mice: A Model for Population-Level scRNA-Seq Studies

Genome-wide association studies (GWASs) have advanced our understanding of the genetics of osteoporosis; however, the challenge has been converting associations to causal genes. Studies have utilized transcriptomics data to link disease-associated variants to genes, but few population transcriptomics data sets have been generated on bone at the single-cell level. To address this challenge, we profiled the transcriptomes of bone marrow-derived stromal cells (BMSCs) cultured under osteogenic conditions from five diversity outbred (DO) mice using single-cell RNA-seq (scRNA-seq). The goal of the study was to determine if BMSCs could serve as a model to generate cell type-specific transcriptomic profiles of mesenchymal lineage cells from large populations of mice to inform genetic studies. By enriching for mesenchymal lineage cells in vitro, coupled with pooling of multiple samples and downstream genotype deconvolution, we demonstrate the scalability of this model for population-level studies. We demonstrate that dissociation of BMSCs from a heavily mineralized matrix had little effect on viability or their transcriptomic signatures. Furthermore, we show that BMSCs cultured under osteogenic conditions are diverse and consist of cells with characteristics of mesenchymal progenitors, marrow adipogenic lineage precursors (MALPs), osteoblasts, osteocyte-like cells, and immune cells. Importantly, all cells were similar from a transcriptomic perspective to cells isolated in vivo. We employed scRNA-seq analytical tools to confirm the biological identity of profiled cell types. SCENIC was used to reconstruct gene regulatory networks (GRNs), and we observed that cell types show GRNs expected of osteogenic and pre-adipogenic lineage cells. Further, CELLECT analysis showed that osteoblasts, osteocyte-like cells, and MALPs captured a significant component of bone mineral density (BMD) heritability. Together, these data suggest that BMSCs cultured under osteogenic conditions coupled with scRNA-seq can be used as a scalable and biologically informative model to generate cell type-specific transcriptomic profiles of mesenchymal lineage cells in large populations. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Variation in histone configurations correlates with gene expression across nine inbred strains of mice

The Diversity Outbred (DO) mice and their inbred founders are widely used models of human disease. However, although the genetic diversity of these mice has been well documented, their epigenetic diversity has not. Epigenetic modifications, such as histone modifications and DNA methylation, are important regulators of gene expression and, as such, are a critical mechanistic link between genotype and phenotype. Therefore, creating a map of epigenetic modifications in the DO mice and their founders is an important step toward understanding mechanisms of gene regulation and the link to disease in this widely used resource. To this end, we performed a strain survey of epigenetic modifications in hepatocytes of the DO founders. We surveyed four histone modifications (H3K4me1, H3K4me3, H3K27me3, and H3K27ac), as well as DNA methylation. We used ChromHMM to identify 14 chromatin states, each of which represents a distinct combination of the four histone modifications. We found that the epigenetic landscape is highly variable across the DO founders and is associated with variation in gene expression across strains. We found that epigenetic state imputed into a population of DO mice recapitulated the association with gene expression seen in the founders, suggesting that both histone modifications and DNA methylation are highly heritable mechanisms of gene expression regulation. We illustrate how DO gene expression can be aligned with inbred epigenetic states to identify putative cis-regulatory regions. Finally, we provide a data resource that documents strain-specific variation in the chromatin state and DNA methylation in hepatocytes across nine widely used strains of laboratory mice.

Vaccines against Group B Coxsackieviruses and Their Importance

The group B coxsackieviruses (CVBs) exist in six serotypes (CVB1 to CVB6). Disease associations have been reported for most serotypes, and multiple serotypes can cause similar diseases. For example, CVB1, CVB3, and CVB5 are generally implicated in the causation of myocarditis, whereas CVB1 and CVB4 could accelerate the development of type 1 diabetes (T1D). Yet, no vaccines against these viruses are currently available. In this review, we have analyzed the attributes of experimentally tested vaccines and discussed their merits and demerits or limitations, as well as their impact in preventing infections, most importantly myocarditis and T1D.

FGF21 is required for protein restriction to extend lifespan and improve metabolic health in male mice

Dietary protein restriction is increasingly recognized as a unique approach to improve metabolic health, and there is increasing interest in the mechanisms underlying this beneficial effect. Recent work indicates that the hormone FGF21 mediates the metabolic effects of protein restriction in young mice. Here we demonstrate that protein restriction increases lifespan, reduces frailty, lowers body weight and adiposity, improves physical performance, improves glucose tolerance, and alters various metabolic markers within the serum, liver, and adipose tissue of wildtype male mice. Conversely, mice lacking FGF21 fail to exhibit metabolic responses to protein restriction in early life, and in later life exhibit early onset of age-related weight loss, reduced physical performance, increased frailty, and reduced lifespan. These data demonstrate that protein restriction in aging male mice exerts marked beneficial effects on lifespan and metabolic health and that a single metabolic hormone, FGF21, is essential for the anti-aging effect of this dietary intervention.

Variation in Host Resistance to Blastomyces dermatitidis: Potential Use of Genetic Reference Panels and Advances in Immunophenotyping of Diverse Mouse Strains

Host genetic determinants that underpin variation in susceptibility to systemic fungal infection are poorly understood. Genes responsible for complex traits can be identified by correlating variation in phenotype with allele in founder strains of wild mice with known genetic variation, assembled in genetic reference panels. In this work, we describe wide natural variation in both primary and acquired resistance to experimental pulmonary blastomycosis in eight founder strains, including 129, A/J, BL/6, CAST, NOD, NZO, PWK, and WSB of the Collaborative Cross collection, and the inbred DBA strain. These differences in susceptibility across strains were accompanied by sharp differences in the accumulation and function of immune cells in the lungs. Immune perturbations were mapped by identifying reagents that phenotypically mark immune cell populations in the distinct strains of mice. In particular, we uncovered marked differences between BL/6 and DBA/2 mouse strains in the development of acquired resistance. Our findings highlight the potential value in using genetic reference panels of mice, and particularly the BXD (recombinant inbred strains of mice from a cross of C57BL/6J and DBA/2J mice) collection harboring a cross between resistant BL/6 and susceptible DBA/2 mice, for unveiling genes linked with host resistance to fungal infection. IMPORTANCE Host genetic variation significantly impacts vulnerability to infectious diseases. While host variation in susceptibility to fungal infection with dimorphic fungi has long been recognized, genes that underpin this variation are poorly understood. We used a collection of seven mouse strains that represent nearly 90% of the genetic variation in mice to identify genetic variability among the strains in resistance to pulmonary infection with the dimorphic fungus Blastomyces dermatitidis. We analyzed differences between the strains in innate resistance by infecting naive mice and in acquired resistance by infecting vaccinated mice. We identified extreme variations in both innate and acquired resistance among the strains. In particular, we found sharp differences between C57BL/6 and DBA/2 strains in the ability to acquire vaccine-induced resistance. We also identified commercial reagents that allowed the phenotyping of immune cells from this strain collection of mice. Because there are additional mice harboring a genetic cross of the C57BL/6 and DBA/2 strains (BXD collection), such mice will permit future investigations to identify the genes that underlie differences in the ability to acquire resistance to infection.

GWAS in Mice Maps Susceptibility to HIV-Associated Nephropathy to the Ssbp2 Locus

BACKGROUND

To gain insight into the pathogenesis of collapsing glomerulopathy, a rare form of FSGS that often arises in the setting of viral infections, we performed a genome-wide association study (GWAS) among inbred mouse strains using a murine model of HIV-1 associated nephropathy (HIVAN).

METHODS

We first generated F1 hybrids between HIV-1 transgenic mice on the FVB/NJ background and 20 inbred laboratory strains. Analysis of histology, BUN, and urinary NGAL demonstrated marked phenotypic variation among the transgenic F1 hybrids, providing strong evidence for host genetic factors in the predisposition to nephropathy. A GWAS in 365 transgenic F1 hybrids generated from these 20 inbred strains was performed.

RESULTS

We identified a genome-wide significant locus on chromosome 13-C3 and multiple additional suggestive loci. Crossannotation of the Chr. 13 locus, including single-cell transcriptomic analysis of wildtype and HIV-1 transgenic mouse kidneys, nominated Ssbp2 as the most likely candidate gene. Ssbp2 is highly expressed in podocytes, encodes a transcriptional cofactor that interacts with LDB1 and LMX1B, which are both previously implicated in FSGS. Consistent with these data, older Ssbp2 null mice spontaneously develop glomerulosclerosis, tubular casts, interstitial fibrosis, and inflammation, similar to the HIVAN mouse model.

CONCLUSIONS

These findings demonstrate the utility of GWAS in mice to uncover host genetic factors for rare kidney traits and suggest Ssbp2 as susceptibility gene for HIVAN, potentially acting via the LDB1-LMX1B transcriptional network.

Utilizing preclinical models of genetic diversity to improve translation of phytochemical activities from rodents to humans and inform personalized nutrition

Mouse models are an essential tool in different areas of research, including nutrition and phytochemical research. Traditional inbred mouse models have allowed the discovery of therapeutical targets and mechanisms of action and expanded our knowledge of health and disease. However, these models lack the genetic variability typically found in human populations, which hinders the translatability of the results found in mice to humans. The development of genetically diverse mouse models, such as the collaborative cross (CC) or the diversity outbred (DO) models, has been a useful tool to overcome this obstacle in many fields, such as cancer, immunology and toxicology. However, these tools have not yet been widely adopted in the field of phytochemical research. As demonstrated in other disciplines, use of CC and DO models has the potential to provide invaluable insights for translation of phytochemicals from rodents to humans, which are desperately needed given the challenges and numerous failed clinical trials in this field. These models may prove informative for personalized use of phytochemicals in humans, including: predicting interindividual variability in phytochemical bioavailability and efficacy, identifying genetic loci or genes governing response to phytochemicals, identifying phytochemical mechanisms of action and therapeutic targets, and understanding the impact of genetic variability on individual response to phytochemicals. Such insights would prove invaluable for personalized implementation of phytochemicals in humans. This review will focus on the current work performed with genetically diverse mouse populations, and the research opportunities and advantages that these models can offer to phytochemical research.

Quantitative Characterization of Population-Wide Tissue- and Metabolite-Specific Variability in Perchloroethylene Toxicokinetics in Male Mice

Quantification of interindividual variability is a continuing challenge in risk assessment, particularly for compounds with complex metabolism and multi-organ toxicity. Toxicokinetic variability for perchloroethylene (perc) was previously characterized across 3 mouse strains and in 1 mouse strain with various degrees of liver steatosis. To further characterize the role of genetic variability in toxicokinetics of perc, we applied Bayesian population physiologically based pharmacokinetic (PBPK) modeling to the data on perc and metabolites in blood/plasma and tissues of male mice from 45 inbred strains from the Collaborative Cross (CC) mouse population. After identifying the most influential PBPK parameters based on global sensitivity analysis, we fit the model with a hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation. We found that the data from 3 commonly used strains were not representative of the full range of variability in perc and metabolite blood/plasma and tissue concentrations across the CC population. Using interstrain variability as a surrogate for human interindividual variability, we calculated dose-dependent, chemical-, and tissue-specific toxicokinetic variability factors (TKVFs) as candidate science-based replacements for the default uncertainty factor for human toxicokinetic variability of 100.5. We found that toxicokinetic variability factors for glutathione conjugation metabolites of perc showed the greatest variability, often exceeding the default, whereas those for oxidative metabolites and perc itself were generally less than the default. Overall, we demonstrate how a combination of a population-based mouse model such as the CC with Bayesian population PBPK modeling can reduce uncertainty in human toxicokinetic variability and increase accuracy and precision in quantitative risk assessment.

The Jackson Laboratory Nathan Shock Center: impact of genetic diversity on aging

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.

Heritability of fat distributions in male mice from the founder strains of the Diversity Outbred mouse population

Specific fat distributions are risk factors for complex diseases, including coronary heart disease and obstructive sleep apnea. To demonstrate the utility of high-diversity mouse models for elucidating genetic associations, we describe the phenotyping and heritability of fat distributions within the five classical inbred and three wild-derived founder mouse strains of the Collaborative Cross and Diversity Outbred mice. Measurements of subcutaneous and internal fat volumes in the abdomen, thorax and neck, and fat volumes in the tongue and pericardium were obtained using magnetic resonance imaging in male mice from the A/J (n = 12), C57BL/6J (n = 17), 129S1/SvlmJ (n = 12), NOD/LtJ (n = 14), NZO/HILtJ (n = 12), CAST/EiJ (n = 14), PWK/PhJ (n = 12), and WSB/EiJ (n = 15) strains. Phenotypes were compared across strains using analysis of variance and heritability estimated as the proportion of phenotypic variability attributable to strain. Heritability ranged from 44 to 91% across traits, including >70% heritability of tongue fat. A majority of heritability estimates remained significant controlling for body weight, suggesting genetic influences independent of general obesity. Principal components analysis supports genetic influences on overall obesity and specific to increased pericardial and intra-neck fat. Thus, among the founder strains of the Collaborative Cross and Diversity Outbred mice, we observed significant heritability of subcutaneous and internal fat volumes in the neck, thorax and abdomen, pericardial fat volume and tongue fat volume, consistent with genetic architecture playing an important role in explaining trait variability. Findings pave the way for studies utilizing high-diversity mouse models to identify genes affecting fat distributions and, in turn, influencing risk for associated complex disorders.

Genes, other than Muc5b, play a role in bleomycin-induced lung fibrosis

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.

In Vitro, In Vivo, and In Silico Methods for Assessment of Muscle Size and Muscle Growth Regulation

Trauma, burn injury, sepsis, and ischemia lead to acute and chronic loss of skeletal muscle mass and function. Healthy muscle is essential for eating, posture, respiration, reproduction, and mobility, as well as for appropriate function of the senses including taste, vision, and hearing. Beyond providing support and contraction, skeletal muscle also exerts essential roles in temperature regulation, metabolism, and overall health. As the primary reservoir for amino acids, skeletal muscle regulates whole-body protein and glucose metabolism by providing substrate for protein synthesis and supporting hepatic gluconeogenesis during illness and starvation. Overall, greater muscle mass is linked to greater insulin sensitivity and glucose disposal, strength, power, and longevity. In contrast, low muscle mass correlates with dysmetabolism, dysmobility, and poor survival. Muscle mass is highly plastic, appropriate to its role as reservoir, and subject to striking genetic control. Defining mechanisms of muscle growth regulation holds significant promise to find interventions that promote health and diminish morbidity and mortality after trauma, sepsis, inflammation, and other systemic insults. In this invited review, we summarize techniques and methods to assess and manipulate muscle size and muscle mass in experimental systems, including cell culture and rodent models. These approaches have utility for studies of myopenia, sarcopenia, cachexia, and acute muscle growth or atrophy in the setting of health or injury.

Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice

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.

Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research

The replicability of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets replicability has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that cannot be replicated. An alternative approach is to, instead, deliberately introduce heterogeneity, known as "heterogenization" of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischemic stroke. First, by quantifying interindividual variability across control groups, we illustrate that the amount of heterogeneity in disease state (infarct volume) differs according to methodological approach, for example, in disease induction methods and disease models. We argue that such methods may improve replicability by creating diverse and representative distribution of baseline disease state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e., low interindividual variability), as well as those where there is high interindividual variability in response; for these, latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimize variability in phenotypic outcomes, we can motivate the shift toward heterogenization and improve both the replicability and generalizability of preclinical research.

Synergizing Mouse and Human Studies to Understand the Heterogeneity of Obesity

Obesity is routinely considered as a single disease state, which drives a "one-size-fits-all" approach to treatment. We recently convened the first annual University of North Carolina Interdisciplinary Nutrition Sciences Symposium to discuss the heterogeneity of obesity and the need for translational science to advance understanding of this heterogeneity. The symposium aimed to advance scientific rigor in translational studies from animal to human models with the goal of identifying underlying mechanisms and treatments. In this review, we discuss fundamental gaps in knowledge of the heterogeneity of obesity ranging from cellular to population perspectives. We also advocate approaches to overcoming limitations in the field. Examples include the use of contemporary mouse genetic reference population models such as the Collaborative Cross and Diversity Outbred mice that effectively model human genetic diversity and the use of translational models that integrate -omics and computational approaches from pre-clinical to clinical models of obesity. Finally, we suggest best scientific practices to ensure strong rigor that will allow investigators to delineate the sources of heterogeneity in the population with obesity. Collectively, we propose that it is critical to think of obesity as a heterogeneous disease with complex mechanisms and etiologies, requiring unique prevention and treatment strategies tailored to the individual.

Simple and reliable detection of CRISPR-induced on-target effects by qgPCR and SNP genotyping

The recent CRISPR revolution has provided researchers with powerful tools to perform genome editing in a variety of organisms. However, recent reports indicate widespread occurrence of unintended CRISPR-induced on-target effects (OnTEs) at the edited site in mice and human induced pluripotent stem cells (iPSCs) that escape standard quality controls. By altering gene expression of targeted or neighbouring genes, OnTEs can severely affect phenotypes of CRISPR-edited cells and organisms and thus lead to data misinterpretation, which can undermine the reliability of CRISPR-based studies. Here we describe a broadly applicable framework for detecting OnTEs in genome-edited cells and organisms after non-homologous end joining-mediated and homology-directed repair-mediated editing. Our protocol enables identification of OnTEs such as large deletions, large insertions, rearrangements or loss of heterozygosity (LOH). This is achieved by subjecting genomic DNA first to quantitative genotyping PCR (qgPCR), which determines the number of intact alleles at the target site using the same PCR amplicon that has been optimized for genotyping. This combination of genotyping and quantitation makes it possible to exclude clones with monoallelic OnTEs and hemizygous editing, which are often mischaracterized as correctly edited in standard Sanger sequencing. Second, occurrence of LOH around the edited locus is detected by genotyping neighbouring single-nucleotide polymorphisms (SNPs), using either a Sanger sequencing-based method or SNP microarrays. All steps are optimized to maximize simplicity and minimize cost to promote wide dissemination and applicability across the field. The entire protocol from genomic DNA extraction to OnTE exclusion can be performed in 6-9 d.

Genetic factors contributing to extensive variability of sex-specific hepatic gene expression in Diversity Outbred mice

Sex-specific transcription characterizes hundreds of genes in mouse liver, many implicated in sex-differential drug and lipid metabolism and disease susceptibility. While the regulation of liver sex differences by growth hormone-activated STAT5 is well established, little is known about autosomal genetic factors regulating the sex-specific liver transcriptome. Here we show, using genotyping and expression data from a large population of Diversity Outbred mice, that genetic factors work in tandem with growth hormone to control the individual variability of hundreds of sex-biased genes, including many long non-coding RNA genes. Significant associations between single nucleotide polymorphisms and sex-specific gene expression were identified as expression quantitative trait loci (eQTLs), many of which showed strong sex-dependent associations. Remarkably, autosomal genetic modifiers of sex-specific genes were found to account for more than 200 instances of gain or loss of sex-specificity across eight Diversity Outbred mouse founder strains. Sex-biased STAT5 binding sites and open chromatin regions with strain-specific variants were significantly enriched at eQTL regions regulating correspondingly sex-specific genes, supporting the proposed functional regulatory nature of the eQTL regions identified. Binding of the male-biased, growth hormone-regulated repressor BCL6 was most highly enriched at trans-eQTL regions controlling female-specific genes. Co-regulated gene clusters defined by overlapping eQTLs included sets of highly correlated genes from different chromosomes, further supporting trans-eQTL action. These findings elucidate how an unexpectedly large number of autosomal factors work in tandem with growth hormone signaling pathways to regulate the individual variability associated with sex differences in liver metabolism and disease.

Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning

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.

Collaborative Cross mice reveal extreme epilepsy phenotypes and genetic loci for seizure susceptibility

OBJECTIVE

Animal studies remain essential for understanding mechanisms of epilepsy and identifying new therapeutic targets. However, existing animal models of epilepsy do not reflect the high level of genetic diversity found in the human population. The Collaborative Cross (CC) population is a genetically diverse recombinant inbred panel of mice. The CC offers large genotypic and phenotypic diversity, inbred strains with stable genomes that allow for repeated phenotypic measurements, and genomic tools including whole genome sequence to identify candidate genes and candidate variants.

METHODS

We evaluated multiple complex epileptic traits in a sampling of 35 CC inbred strains using the flurothyl-induced seizure and kindling paradigm. We created an F2 population of 297 mice with extreme seizure susceptibility and performed quantitative trait loci (QTL) mapping to identify genomic regions associated with seizure sensitivity. We used quantitative RNA sequencing from CC hippocampal tissue to identify candidate genes and whole genome sequence to identify genetic variants likely affecting gene expression.

RESULTS

We identified new mouse models with extreme seizure susceptibility, seizure propagation, epileptogenesis, and SUDEP (sudden unexpected death in epilepsy). We performed QTL mapping and identified one known and seven novel loci associated with seizure sensitivity. We combined whole genome sequencing and hippocampal gene expression to pinpoint biologically plausible candidate genes (eg, Gabra2) and variants associated with seizure sensitivity.

SIGNIFICANCE

New mouse models of epilepsy are needed to better understand the complex genetic architecture of seizures and to identify therapeutics. We performed a phenotypic screen utilizing a novel genetic reference population of CC mice. The data we provide enable the identification of protective/risk genes and novel molecular mechanisms linked to complex seizure traits that are currently challenging to study and treat.

Reproducibility of animal research in light of biological variation

Context-dependent biological variation presents a unique challenge to the reproducibility of results in experimental animal research, because organisms' responses to experimental treatments can vary with both genotype and environmental conditions. In March 2019, experts in animal biology, experimental design and statistics convened in Blonay, Switzerland, to discuss strategies addressing this challenge. In contrast to the current gold standard of rigorous standardization in experimental animal research, we recommend the use of systematic heterogenization of study samples and conditions by actively incorporating biological variation into study design through diversifying study samples and conditions. Here we provide the scientific rationale for this approach in the hope that researchers, regulators, funders and editors can embrace this paradigm shift. We also present a road map towards better practices in view of improving the reproducibility of animal research.

Mouse Phenome Database: a data repository and analysis suite for curated primary mouse phenotype data

The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely accessed and highly functional data repository housing primary phenotype data for the laboratory mouse accessible via APIs and providing tools to analyze and visualize those data. Data come from investigators around the world and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD houses rigorously curated per-animal data with detailed protocols. Public ontologies and controlled vocabularies are used for annotation. In addition to phenotype tools, genetic analysis tools enable users to integrate and interpret genome–phenome relations across the database. Strain types and populations include inbred, recombinant inbred, F1 hybrid, transgenic, targeted mutants, chromosome substitution, Collaborative Cross, Diversity Outbred and other mapping populations. Our new analysis tools allow users to apply selected data in an integrated fashion to address problems in trait associations, reproducibility, polygenic syndrome model selection and multi-trait modeling. As we refine these tools and approaches, we will continue to provide users a means to identify consistent, quality studies that have high translational relevance.

Leveraging preclinical models for the development of Alzheimer disease therapeutics

A large number of mouse models have been engineered, characterized and used to advance biomedical research in Alzheimer disease (AD). Early models simply damaged the rodent brain through toxins or lesions. Later, the spread of genetic engineering technology enabled investigators to develop models of familial AD by overexpressing human genes such as those encoding amyloid precursor protein (APP) or presenilins (PSEN1 or PSEN2) carrying mutations linked to early-onset AD. Recently, more complex models have sought to explore the impact of multiple genetic risk factors in the context of different biological challenges. Although none of these models has proven to be a fully faithful reproduction of the human disease, models remain essential as tools to improve our understanding of AD biology, conduct thorough pharmacokinetic and pharmacodynamic analyses, discover translatable biomarkers and evaluate specific therapeutic approaches. To realize the full potential of animal models as new technologies and knowledge become available, it is critical to define an optimal strategy for their use. Here, we review progress and challenges in the use of AD mouse models, highlight emerging scientific innovations in model development, and introduce a conceptual framework for use of preclinical models for therapeutic development.

Genomic mapping in outbred mice reveals overlap in genetic susceptibility for HZE ion- and γ-ray-induced tumors

Cancer risk from galactic cosmic radiation exposure is considered a potential "showstopper" for a manned mission to Mars. Calculating the actual risks confronted by spaceflight crews is complicated by our limited understanding of the carcinogenic effects of high-charge, high-energy (HZE) ions, a radiation type for which no human exposure data exist. Using a mouse model of genetic diversity, we find that the histotype spectrum of HZE ion-induced tumors is similar to the spectra of spontaneous and γ-ray-induced tumors and that the genomic loci controlling susceptibilities overlap between groups for some tumor types. Where it occurs, this overlap indicates shared tumorigenesis mechanisms regardless of the type of radiation exposure and supports the use of human epidemiological data from γ-ray exposures to predict cancer risks from galactic cosmic rays.

Methods for evaluating variability in human health dose-response characterization

The Reference Dose (RfD) and Reference Concentration (RfC) are human health reference values (RfVs) representing exposure concentrations at or below which there is presumed to be little risk of adverse effects in the general human population. The 2009 National Research Council report Science and Decisions recommended redefining RfVs as "a risk-specific dose (for example, the dose associated with a 1 in 100,000 risk of a particular end point)." Distributions representing variability in human response to environmental contaminant exposures are critical for deriving risk-specific doses. Existing distributions estimating the extent of human toxicokinetic and toxicodynamic variability are based largely on controlled human exposure studies of pharmaceuticals. New data and methods have been developed that are designed to improve estimation of the quantitative variability in human response to environmental chemical exposures. Categories of research with potential to provide new database useful for developing updated human variability distributions include controlled human experiments, human epidemiology, animal models of genetic variability, in vitro estimates of toxicodynamic variability, and in vitro-based models of toxicokinetic variability. In vitro approaches, with further development including studies of different cell types and endpoints, and approaches to incorporate non-genetic sources of variability, appear to provide the greatest opportunity for substantial near-term advances.

Systems genetics applications in metabolism research

The common forms of metabolic diseases are highly complex, involving hundreds of genes, environmental and lifestyle factors, age-related changes, sex differences and gut-microbiome interactions. Systems genetics is a population-based approach to address this complexity. In contrast to commonly used 'reductionist' approaches, such as gain or loss of function, that examine one element at a time, systems genetics uses high-throughput 'omics' technologies to quantitatively assess the many molecular differences among individuals in a population and then to relate these to physiologic functions or disease states. Unlike genome-wide association studies, systems genetics seeks to go beyond the identification of disease-causing genes to understand higher-order interactions at the molecular level. The purpose of this review is to introduce the systems genetics applications in the areas of metabolic and cardiovascular disease. Here, we explain how large clinical and omics-level data and databases from both human and animal populations are available to help researchers place genes in the context of pathways and networks and formulate hypotheses that can then be experimentally examined. We provide lists of such databases and examples of the integration of reductionist and systems genetics data. Among the important applications emerging is the development of improved nutritional and pharmacological strategies to address the rise of metabolic diseases.

Mouse Phenome Database: an integrative database and analysis suite for curated empirical phenotype data from laboratory mice

The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely used resource that provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD houses individual animal data with detailed, searchable protocols, and makes these data available to other resources via API. MPD provides rigorous curation of experimental data and supporting documentation using relevant ontologies and controlled vocabularies. Most data in MPD are from inbreds and other reproducible strains such that the data are cumulative over time and across laboratories. The resource has been expanded to include the QTL Archive and other primary phenotype data from mapping crosses as well as advanced high-diversity mouse populations including the Collaborative Cross and Diversity Outbred mice. Furthermore, MPD provides a means of assessing replicability and reproducibility across experimental conditions and protocols, benchmarking assays in users’ own laboratories, identifying sensitized backgrounds for making new mouse models with genome editing technologies, analyzing trait co-inheritance, finding the common genetic basis for multiple traits and assessing sex differences and sex-by-genotype interactions.

Gene-by-environment interactions in Alzheimer's disease and Parkinson's disease

Diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) arise from complex interactions of genetic and environmental factors, with genetic variants regulating individual responses to environmental exposures (i.e. gene-by-environment interactions). Identifying gene-by-environment interactions will be critical to fully understanding disease mechanisms and developing personalized therapeutics, though these interactions are still poorly understood and largely under-studied. Candidate gene approaches have shown that known disease risk variants often regulate response to environmental factors. However, recent improvements in exposome- and genome-wide association and interaction studies in humans and mice are enabling discovery of novel genetic variants and pathways that predict response to a variety of environmental factors. Here, we highlight recent approaches and ongoing developments in human and rodent studies to identify genetic modulators of environmental factors using AD and PD as exemplars. Identifying gene-by-environment interactions in disease will be critical to developing personalized intervention strategies and will pave the way for precision medicine.

Meiosis: the chromosomal foundation of reproduction

Meiosis is the chromosomal foundation of reproduction, with errors in this important process leading to aneuploidy and/or infertility. In this review celebrating the 50th anniversary of the founding of the Society for the Study of Reproduction, the important chromosomal structures and dynamics contributing to genomic integrity across generations are highlighted. Critical unsolved biological problems are identified, and the advances that will lead to their ultimate resolution are predicted.

Population-Based Analysis of DNA Damage and Epigenetic Effects of 1,3-Butadiene in the Mouse

Metabolism of 1,3-butadiene, a known human and rodent carcinogen, results in formation of reactive epoxides, a key event in its carcinogenicity. Although mice exposed to 1,3-butadiene present DNA adducts in all tested tissues, carcinogenicity is limited to liver, lung, and lymphoid tissues. Previous studies demonstrated that strain- and tissue-specific epigenetic effects in response to 1,3-butadiene exposure may influence susceptibly to DNA damage and serve as a potential mechanism of tissue-specific carcinogenicity. This study aimed to investigate interindividual variability in the effects of 1,3-butadiene using a population-based mouse model. Male mice from 20 Collaborative Cross strains were exposed to 0 or 635 ppm 1,3-butadiene by inhalation (6 h/day, 5 days/week) for 2 weeks. We evaluated DNA damage and epigenetic effects in target (lung and liver) and nontarget (kidney) tissues of 1,3-butadiene-induced carcinogenesis. DNA damage was assessed by measuring N-7-(2,3,4-trihydroxybut-1-yl)-guanine (THB-Gua) adducts. To investigate global histone modification alterations, we evaluated the trimethylation and acetylation of histones H3 and H4 across tissues. Changes in global cytosine DNA methylation were evaluated from the levels of methylation of LINE-1 and SINE B1 retrotransposons. We quantified the degree of variation across strains, deriving a chemical-specific human variability factor to address population variability in carcinogenic risk, which is largely ignored in current cancer risk assessment practice. Quantitative trait locus mapping identified four candidate genes related to chromatin remodeling whose variation was associated with interstrain susceptibility. Overall, this study uses 1,3-butadiene to demonstrate how the Collaborative Cross mouse population can be used to identify the mechanisms for and quantify the degree of interindividual variability in tissue-specific effects that are relevant to chemically induced carcinogenesis.

The Genetics of Aging: A Vertebrate Perspective

Aging negatively impacts vitality and health. Many genetic pathways that regulate aging were discovered in invertebrates. However, the genetics of aging is more complex in vertebrates because of their specialized systems. This Review discusses advances in the genetic regulation of aging in vertebrates from work in mice, humans, and organisms with exceptional lifespans. We highlight challenges for the future, including sex-dependent differences in lifespan and the interplay between genes and environment. We also discuss how the identification of reliable biomarkers of age and development of new vertebrate models can be leveraged for personalized interventions to counter aging and age-related diseases.

Mast cells as sources of cytokines, chemokines, and growth factors

Mast cells are hematopoietic cells that reside in virtually all vascularized tissues and that represent potential sources of a wide variety of biologically active secreted products, including diverse cytokines and growth factors. There is strong evidence for important non-redundant roles of mast cells in many types of innate or adaptive immune responses, including making important contributions to immediate and chronic IgE-associated allergic disorders and enhancing host resistance to certain venoms and parasites. However, mast cells have been proposed to influence many other biological processes, including responses to bacteria and virus, angiogenesis, wound healing, fibrosis, autoimmune and metabolic disorders, and cancer. The potential functions of mast cells in many of these settings is thought to reflect their ability to secrete, upon appropriate activation by a range of immune or non-immune stimuli, a broad spectrum of cytokines (including many chemokines) and growth factors, with potential autocrine, paracrine, local, and systemic effects. In this review, we summarize the evidence indicating which cytokines and growth factors can be produced by various populations of rodent and human mast cells in response to particular immune or non-immune stimuli, and comment on the proven or potential roles of such mast cell products in health and disease.

Principles of precision medicine and its application in toxicology

Precision medicine is an approach to developing drugs that focuses on employing biomarkers to stratify patients in clinical trials with the goal of improving efficacy and/or safety outcomes, ultimately increasing the odds of clinical success and drug approval. Precision medicine is an important tool for toxicologists to utilize, because its principles can be used to decide whether to pursue a drug target, to understand interindividual differences in response to drugs in both nonclinical and clinical settings, to aid in selecting doses that optimize efficacy or reduce adverse events, and to facilitate understanding of a drug's mode-of-action. Nonclinical models such as the mouse and non-human primate can be used to understand genetic variation and its potential translation to humans, and are available for toxicologists to employ in advance of drugs moving into clinical development. Understanding interindividual differences in response to drugs and how these differences can influence the drug's risk-benefit profile and lead to the identification of biomarkers that enhance patient efficacy and safety is of critical importance for toxicologists today, and in the future, as the fields of pharmacogenomics and genetics continue to advance.

Easy Access to and Applications of the Sequences of All Protein-Coding Genes of All Sequenced Mouse Strains

An easily accessible and searchable overview of all protein sequences in the 36 genome-sequenced mouse strains, compared to those in the reference strain C57BL/6J, is now available, as well as an overview of the aberrant proteins in this reference strain. We provide an insight into the advantages of using these databases.

Modeling Diversity: Do Homogeneous Laboratory Strains Limit Discovery?

The outcome of chronic infections is highly variable. The heterogeneous disease outcomes in natural populations differ from genetically homogeneous infection models. Here, we use tuberculosis as a 'case study' to contrast the genetic landscape in natural populations with standard infection models, discussing new strategies to bridge this gap.

Editor's Highlight: Collaborative Cross Mouse Population Enables Refinements to Characterization of the Variability in Toxicokinetics of Trichloroethylene and Provides Genetic Evidence for the Role of PPAR Pathway in Its Oxidative Metabolism

Background

Trichloroethylene (TCE) is a known carcinogen in humans and rodents. Previous studies of inter-strain variability in TCE metabolism were conducted in multi-strain panels of classical inbred mice with limited genetic diversity to identify gene-environment interactions associated with chemical exposure.

Objectives

To evaluate inter-strain variability in TCE metabolism and identify genetic determinants that are associated with TCE metabolism and effects using Collaborative Cross (CC), a large panel of genetically diverse strains of mice.

Methods

We administered a single oral dose of 0, 24, 80, 240, or 800 mg/kg of TCE to mice from 50 CC strains, and collected organs 24 h post-dosing. Levels of trichloroacetic acid (TCA), a major oxidative metabolite of TCE were measured in multiple tissues. Protein expression and activity levels of TCE-metabolizing enzymes were evaluated in the liver. Liver transcript levels of known genes perturbed by TCE exposure were also quantified. Genetic association mapping was performed on the acquired phenotypes.

Results

TCA levels varied in a dose- and strain-dependent manner in liver, kidney, and serum. The variability in TCA levels among strains did not correlate with expression or activity of a number of enzymes known to be involved in TCE oxidation. Peroxisome proliferator-activated receptor alpha (PPARα)-responsive genes were found to be associated with strain-specific differences in TCE metabolism.

Conclusions

This study shows that CC mouse population is a valuable tool to quantitatively evaluate inter-individual variability in chemical metabolism and to identify genes and pathways that may underpin population differences.

CC002/Unc females are mouse models of exercise-induced paradoxical fat response

Exercise results in beneficial health outcomes and protects against a variety of chronic diseases. However, U.S. exercise guidelines recommend identical exercise programs for everyone, despite individual variation in responses to these programs, including paradoxical fat gain. Experimental models of exercise-induced paradoxical outcomes may enable the dissection of underlying physiological mechanisms as well as the evaluation of potential interventions. Whereas several studies have identified individual mice exhibiting paradoxical fat gain following exercise, no systematic effort has been conducted to identify and characterize models of paradoxical response. Strains from the Collaborative Cross (CC) genetic reference population were used due to its high levels of genetic variation, its reproducible nature, and the observation that the CC is a rich source of novel disease models, to assess the impact genetic background has on exercise responses. We identified the strain CC002/Unc as an exercise-induced paradoxical fat response model in a controlled voluntary exercise study across multiple ages in female mice. We also found sex and genetic differences were consistent with this pattern in a study of forced exercise programs. These results provide a novel model for studies to determine the mechanisms behind paradoxical metabolic responses to exercise, and enable development of more rational personalized exercise recommendations based on factors such as age, sex, and genetic background.