Discovery and validation of genes driving drug-intake and related behavioral traits in mice.

Substance use disorders are heritable disorders characterized by compulsive drug use, the biological mechanisms for which remain largely unknown. Genetic correlations reveal that predisposing drug-naïve phenotypes, including anxiety, depression, novelty preference and sensation seeking, are predictive of drug-use phenotypes, thereby implicating shared genetic mechanisms. High-throughput behavioral screening in knockout (KO) mice allows efficient discovery of the function of genes. We used this strategy in two rounds of candidate prioritization in which we identified 33 drug-use candidate genes based upon predisposing drug-naïve phenotypes and ultimately validated the perturbation of 22 genes as causal drivers of substance intake. We selected 19/221 KO strains (8.5%) that had a difference from control on at least one drug-naïve predictive behavioral phenotype and determined that 15/19 (~80%) affected the consumption or preference for alcohol, methamphetamine or both. No mutant exhibited a difference in nicotine consumption or preference which was possibly confounded with saccharin. In the second round of prioritization, we employed a multivariate approach to identify outliers and performed validation using methamphetamine two-bottle choice and ethanol drinking-in-the-dark protocols. We identified 15/401 KO strains (3.7%, which included one gene from the first cohort) that differed most from controls for the predisposing phenotypes. 8 of 15 gene deletions (53%) affected intake or preference for alcohol, methamphetamine or both. Using multivariate and bioinformatic analyses, we observed multiple relations between predisposing behaviors and drug intake, revealing many distinct biobehavioral processes underlying these relationships. The set of mouse models identified in this study can be used to characterize these addiction-related processes further.

DISCOVERY AND VALIDATION OF GENES DRIVING DRUG-INTAKE AND RELATED BEHAVIORAL TRAITS IN MICE.

Substance use disorders (SUDs) are heritable disorders characterized by compulsive drug use, but the biological mechanisms driving addiction remain largely unknown. Genetic correlations reveal that predisposing drug-naïve phenotypes, including anxiety, depression, novelty preference, and sensation seeking, are predictive of drug-use phenotypes, implicating shared genetic mechanisms. Because of this relationship, high-throughput behavioral screening of predictive phenotypes in knockout (KO) mice allows efficient discovery of genes likely to be involved in drug use. We used this strategy in two rounds of screening in which we identified 33 drug-use candidate genes and ultimately validated the perturbation of 22 of these genes as causal drivers of substance intake. In our initial round of screening, we employed the two-bottle-choice paradigms to assess alcohol, methamphetamine, and nicotine intake. We identified 19 KO strains that were extreme responders on at least one predictive phenotype. Thirteen of the 19 gene deletions (68%) significantly affected alcohol use three methamphetamine use, and two both. In the second round of screening, we employed a multivariate approach to identify outliers and performed validation using methamphetamine two-bottle choice and ethanol drinking-in-the-dark protocols. We identified 15 KO strains that were extreme responders across the predisposing drug-naïve phenotypes. Eight of the 15 gene deletions (53%) significantly affected intake or preference for three alcohol, eight methamphetamine or three both (3). We observed multiple relations between predisposing behaviors and drug intake, revealing many distinct biobehavioral processes underlying these relationships. The set of mouse models identified in this study can be used to characterize these addiction-related processes further.

Animals, quality and the pursuit of relevance.

In 2021, the National Institutes of Health Advisory Committee to the Director (ACD) announced recommendations to improve the reproducibility of biomedical research using animals. In response, The Jackson Laboratory faculty and institutional leaders identified key strategies to further address this important issue. Taking inspiration from the evolution of clinical trials over recent decades in response to similar challenges, we identified opportunities for improvement, including establishment of common standards, use of genetically diverse populations, requirement for robust study design with appropriate statistical methods, and improvement in public databases to facilitate meta-analyses. In this Perspective, we share our response to ACD recommendations, with a specific focus on mouse models, with the aim of promoting continued active dialogue among researchers, using any animal system, worldwide. Such discussion will help to inform the biomedical community about these recommendations and further support their much-needed implementation.

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence.

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

Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice.

We compared the performance of linear (GBLUP, BayesB, and elastic net) methods to a nonparametric tree-based ensemble (gradient boosting machine) method for genomic prediction of complex traits in mice. The dataset used contained genotypes for 50,112 SNP markers and phenotypes for 835 animals from 6 generations. Traits analyzed were bone mineral density, body weight at 10, 15, and 20 weeks, fat percentage, circulating cholesterol, glucose, insulin, triglycerides, and urine creatinine. The youngest generation was used as a validation subset, and predictions were based on all older generations. Model performance was evaluated by comparing predictions for animals in the validation subset against their adjusted phenotypes. Linear models outperformed gradient boosting machine for 7 out of 10 traits. For bone mineral density, cholesterol, and glucose, the gradient boosting machine model showed better prediction accuracy and lower relative root mean squared error than the linear models. Interestingly, for these 3 traits, there is evidence of a relevant portion of phenotypic variance being explained by epistatic effects. Using a subset of top markers selected from a gradient boosting machine model helped for some of the traits to improve the accuracy of prediction when these were fitted into linear and gradient boosting machine models. Our results indicate that gradient boosting machine is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Although the linear models outperformed gradient boosting machine for the polygenic traits, our results suggest that gradient boosting machine is a competitive method to predict complex traits with assumed epistatic effects.

The dihydropyrimidine dehydrogenase gene contributes to heritable differences in sleep in mice.

Many aspects of sleep are heritable, but only a few sleep-regulating genes have been reported. Here, we leverage mouse models to identify and confirm a previously unreported gene affecting sleep duration-dihydropyrimidine dehydrogenase (Dpyd). Using activity patterns to quantify sleep in 325 Diversity Outbred (DO) mice-a population with high genetic and phenotypic heterogeneity-a linkage peak for total sleep in the active lights off period was identified on chromosome 3 (LOD score = 7.14). Mice with the PWK/PhJ ancestral haplotype at this location demonstrated markedly reduced sleep. Among the genes within the linkage region, available RNA sequencing data in an independent sample of DO mice supported a highly significant expression quantitative trait locus for Dpyd, wherein reduced expression was associated with the PWK/PhJ allele. Validation studies were performed using activity monitoring and EEG/EMG recording in Collaborative Cross mouse strains with and without the PWK/PhJ haplotype at this location, as well as EEG and EMG recording of sleep and wake in Dpyd knockout mice and wild-type littermate controls. Mice lacking Dpyd had 78.4 min less sleep during the lights-off period than wild-type mice (p = 0.007; Cohen's d = -0.94). There was no difference in other measured behaviors in knockout mice, including assays evaluating cognitive-, social-, and affective-disorder-related behaviors. Dpyd encodes the rate-limiting enzyme in the metabolic pathway that catabolizes uracil and thymidine to ß-alanine, an inhibitory neurotransmitter. Thus, data support ß-alanine as a neurotransmitter that promotes sleep in mice.

Shared and distinctive features of the gut microbiome of C57BL/6 mice from different vendors and production sites, and in response to a new vivarium.

Animal models play a critical role in establishing causal relationships between gut microbiota and disease. The laboratory mouse is widely used to study the role of microbes in various disorders; however, differences between mouse vendors, genetic lineages and husbandry protocols have been shown to contribute to variation in phenotypes and to non-reproducibility of experimental results. We sought to understand how gut microbiome profiles of mice vary by vendor, vendor production facility and health status upon receipt into an academic facility and how they change over 12 weeks in the new environment. C57BL/6 mice were sourced from two different production sites for each of three different vendors. Mice were shipped to an academic research vivarium, and fresh-catch stool samples were collected from mice immediately from the shipping box upon receipt, and again after 2, 6 and 12 weeks in the new facility. Substantial variation in bacterial proportional abundance was observed among mice from each vendor at the time of receipt, but shared microbes accounted for most sequence reads. Vendor-specific microbes were generally of low abundance. Microbial profiles of mice from all vendors exhibited shifts over time, highlighting the importance of environmental conditions on microbial dynamics. Our results emphasize the need for continued efforts to account for sources of variation in animal models and understand how they contribute to experimental reproducibility.

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.

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease.

High-throughput sleep phenotyping produces robust and heritable traits in Diversity Outbred mice and their founder strains.

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.

A mutation in mouse Krüppel-like factor 15 alters the gut microbiome and response to obesogenic diet.

We identified a mouse strain, HLB444, carrying an N-ethyl-N-nitrosourea (ENU)-induced mutation in a highly conserved C2H2 zinc-finger DNA binding motif of the transcriptional regulator KLF15 that exhibits resistance to diet-induced obesity. Characterization of the HLB444 mutant model on high-fat and chow diets revealed a number of phenotypic differences compared to wild-type controls. When fed a high fat diet, HLB444 had lower body fat, resistance to hepatosteatosis, lower circulating glucose and improved insulin sensitivity compared to C57BL/6J controls. Gut microbial profiles in HLB444 generated from 16S rRNA sequencing of fecal samples differed from controls under both chow and high fat diets. HLB444 shares similar phenotypic traits with engineered full- and adipose-specific Klf15 knockout strains; however, some phenotypic differences between this mutant and the other models suggest that the Klf15 mutation in HLB444 is a hypomorphic variant. The HLB444 model will inform further annotation of transcriptional functions of KLF15, especially with respect to the role of the first zinc-finger domain.

Drug safety Africa: An overview of safety pharmacology & toxicology in South Africa.

This meeting report is based on presentations given at the first Drug Safety Africa Meeting in Potchefstroom, South Africa from November 20-22, 2018 at the North-West University campus. There were 134 attendees (including 26 speakers and 34 students) from the pharmaceutical industry, academia, regulatory agencies as well as 6 exhibitors. These meeting proceedings are designed to inform the content that was presented in terms of Safety Pharmacology (SP) and Toxicology methods and models that are used by the pharmaceutical industry to characterize the safety profile of novel small chemical or biological molecules. The first part of this report includes an overview of the core battery studies defined by cardiovascular, central nervous system (CNS) and respiratory studies. Approaches to evaluating drug effects on the renal and gastrointestinal systems and murine phenotyping were also discussed. Subsequently, toxicological approaches were presented including standard strategies and options for early identification and characterization of risks associated with a novel therapeutic, the types of toxicology studies conducted and relevance to risk assessment supporting first-in-human (FIH) clinical trials and target organ toxicity. Biopharmaceutical development and principles of immunotoxicology were discussed as well as emerging technologies. An additional poster session was held that included 18 posters on advanced studies and topics by South African researchers, postgraduate students and postdoctoral fellows.

Cleaning Genotype Data from Diversity Outbred Mice.

Data cleaning is an important first step in most statistical analyses, including efforts to map the genetic loci that contribute to variation in quantitative traits. Here we illustrate approaches to quality control and cleaning of array-based genotyping data for multiparent populations (experimental crosses derived from more than two founder strains), using MegaMUGA array data from a set of 291 Diversity Outbred (DO) mice. Our approach employs data visualizations that can reveal problems at the level of individual mice or with individual SNP markers. We find that the proportion of missing genotypes for each mouse is an effective indicator of sample quality. We use microarray probe intensities for SNPs on the X and Y chromosomes to confirm the sex of each mouse, and we use the proportion of matching SNP genotypes between pairs of mice to detect sample duplicates. We use a hidden Markov model (HMM) reconstruction of the founder haplotype mosaic across each mouse genome to estimate the number of crossovers and to identify potential genotyping errors. To evaluate marker quality, we find that missing data and genotyping error rates are the most effective diagnostics. We also examine the SNP genotype frequencies with markers grouped according to their minor allele frequency in the founder strains. For markers with high apparent error rates, a scatterplot of the allele-specific probe intensities can reveal the underlying cause of incorrect genotype calls. The decision to include or exclude low-quality samples can have a significant impact on the mapping results for a given study. We find that the impact of low-quality markers on a given study is often minimal, but reporting problematic markers can improve the utility of the genotyping array across many studies.

Erratum: Author Correction: Identification of genes required for eye development by high-throughput screening of mouse knockouts.

[This corrects the article DOI: 10.1038/s42003-018-0226-0.].

Recommended housing densities for research mice: filling the gap in data-driven alternatives.

Space recommendations for mice made in the Guide for Care and Use of Laboratory Animals have not changed since 1972, despite important improvements in husbandry and caging practices. The 1996 version of the Guide put forth a challenge to investigators to produce new data evaluating the effects of space allocation on the well-being of mice. In this review, we summarize many studies published in response to this challenge. We distinguish between studies using ventilated or nonventilated caging systems and those evaluating reproductive performance or general well-being of adult mice. We discuss how these studies might affect current housing density considerations in both production and research settings and consider gaps in mouse housing density research. Additionally, we discuss reliable methods used to monitor and quantify general well-being of research mice. Collectively, this large body of new data suggests that husbandry practices dictating optimal breeding schemes and space allocation per mouse can be reconsidered. Specifically, these data demonstrate that prewean culling of litters has no benefit, trio breeding is an effective production strategy without adversely affecting pup survival and well-being, and housing of adult mice at densities of up to twice current Guide recommendations does not compromise well-being for most strains.-Svenson, K. L., Paigen, B. Recommended housing densities for research mice: filling the gap in data-driven alternatives.

Identification of genes required for eye development by high-throughput screening of mouse knockouts.

Despite advances in next generation sequencing technologies, determining the genetic basis of ocular disease remains a major challenge due to the limited access and prohibitive cost of human forward genetics. Thus, less than 4,000 genes currently have available phenotype information for any organ system. Here we report the ophthalmic findings from the International Mouse Phenotyping Consortium, a large-scale functional genetic screen with the goal of generating and phenotyping a null mutant for every mouse gene. Of 4364 genes evaluated, 347 were identified to influence ocular phenotypes, 75% of which are entirely novel in ocular pathology. This discovery greatly increases the current number of genes known to contribute to ophthalmic disease, and it is likely that many of the genes will subsequently prove to be important in human ocular development and disease.

Erratum: "Diversity Outbred Mice Identify Population-Based Exposure Thresholds and Genetic Factors that Influence Benzene-Induced Genotoxicity".

[This corrects the article DOI: http://dx.doi.org/10.1289/ehp.1408202.].

Corrigendum: Genetic identification of thiosulfate sulfurtransferase as an adipocyte-expressed antidiabetic target in mice selected for leanness.

This corrects the article DOI: 10.1038/nm.4115.