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.

Defining the consequences of genetic variation on a proteome-wide scale.

Genetic variation modulates protein expression through both transcriptional and post-transcriptional mechanisms. To characterize the consequences of natural genetic diversity on the proteome, here we combine a multiplexed, mass spectrometry-based method for protein quantification with an emerging outbred mouse model containing extensive genetic variation from eight inbred founder strains. By measuring genome-wide transcript and protein expression in livers from 192 Diversity outbred mice, we identify 2,866 protein quantitative trait loci (pQTL) with twice as many local as distant genetic variants. These data support distinct transcriptional and post-transcriptional models underlying the observed pQTL effects. Using a sensitive approach to mediation analysis, we often identified a second protein or transcript as the causal mediator of distant pQTL. Our analysis reveals an extensive network of direct protein-protein interactions. Finally, we show that local genotype can provide accurate predictions of protein abundance in an independent cohort of collaborative cross mice.

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.

APOBEC1 complementation factor (A1CF) is dispensable for C-to-U RNA editing in vivo.

Editing of the human and murine ApoB mRNA by APOBEC1, the catalytic enzyme of the protein complex that catalyzes C-to-U RNA editing, creates an internal stop codon within the APOB coding sequence, generating two protein isoforms. It has been long held that APOBEC1-mediated editing activity is dependent on the RNA binding protein A1CF. The function of A1CF in adult tissues has not been reported because a previously reported null allele displays embryonic lethality. This work aimed to address the function of A1CF in adult mouse tissues using a conditional A1cf allele. Unexpectedly, A1cf-null mice were viable and fertile with modest defects in hematopoietic, immune, and metabolic parameters. C-to-U RNA editing was quantified for multiple targets, including ApoB, in the small intestine and liver. In all cases, no changes in RNA editing efficiency were observed. Blood plasma analysis demonstrated a male-specific increase in solute concentration and increased cellularity in the glomeruli of male A1cf-null mice. Urine analysis showed a reduction in solute concentration, suggesting abnormal water homeostasis and possible kidney abnormalities exclusive to the male. Computational identification of kidney C-to-U editing sites from polyadenylated RNA-sequencing identified a number of editing sites exclusive to the kidney. However, molecular analysis of kidney C-to-U editing showed no changes in editing efficiency with A1CF loss. Taken together, these observations demonstrate that A1CF does not act as the APOBEC1 complementation factor in vivo under normal physiological conditions and suggests new roles for A1CF, specifically within the male adult kidney.

Applying the ARRIVE Guidelines to an In Vivo Database.

The Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were developed to address the lack of reproducibility in biomedical animal studies and improve the communication of research findings. While intended to guide the preparation of peer-reviewed manuscripts, the principles of transparent reporting are also fundamental for in vivo databases. Here, we describe the benefits and challenges of applying the guidelines for the International Mouse Phenotyping Consortium (IMPC), whose goal is to produce and phenotype 20,000 knockout mouse strains in a reproducible manner across ten research centres. In addition to ensuring the transparency and reproducibility of the IMPC, the solutions to the challenges of applying the ARRIVE guidelines in the context of IMPC will provide a resource to help guide similar initiatives in the future.

A multi-megabase copy number gain causes maternal transmission ratio distortion on mouse chromosome 2.

Significant departures from expected Mendelian inheritance ratios (transmission ratio distortion, TRD) are frequently observed in both experimental crosses and natural populations. TRD on mouse Chromosome (Chr) 2 has been reported in multiple experimental crosses, including the Collaborative Cross (CC). Among the eight CC founder inbred strains, we found that Chr 2 TRD was exclusive to females that were heterozygous for the WSB/EiJ allele within a 9.3 Mb region (Chr 2 76.9 - 86.2 Mb). A copy number gain of a 127 kb-long DNA segment (designated as responder to drive, R2d) emerged as the strongest candidate for the causative allele. We mapped R2d sequences to two loci within the candidate interval. R2d1 is located near the proximal boundary, and contains a single copy of R2d in all strains tested. R2d2 maps to a 900 kb interval, and the number of R2d copies varies from zero in classical strains (including the mouse reference genome) to more than 30 in wild-derived strains. Using real-time PCR assays for the copy number, we identified a mutation (R2d2WSBdel1) that eliminates the majority of the R2d2WSB copies without apparent alterations of the surrounding WSB/EiJ haplotype. In a three-generation pedigree segregating for R2d2WSBdel1, the mutation is transmitted to the progeny and Mendelian segregation is restored in females heterozygous for R2d2WSBdel1, thus providing direct evidence that the copy number gain is causal for maternal TRD. We found that transmission ratios in R2d2WSB heterozygous females vary between Mendelian segregation and complete distortion depending on the genetic background, and that TRD is under genetic control of unlinked distorter loci. Although the R2d2WSB transmission ratio was inversely correlated with average litter size, several independent lines of evidence support the contention that female meiotic drive is the cause of the distortion. We discuss the implications and potential applications of this novel meiotic drive system.

R2d2 Drives Selfish Sweeps in the House Mouse.

A selective sweep is the result of strong positive selection driving newly occurring or standing genetic variants to fixation, and can dramatically alter the pattern and distribution of allelic diversity in a population. Population-level sequencing data have enabled discoveries of selective sweeps associated with genes involved in recent adaptations in many species. In contrast, much debate but little evidence addresses whether "selfish" genes are capable of fixation-thereby leaving signatures identical to classical selective sweeps-despite being neutral or deleterious to organismal fitness. We previously described R2d2, a large copy-number variant that causes nonrandom segregation of mouse Chromosome 2 in females due to meiotic drive. Here we show population-genetic data consistent with a selfish sweep driven by alleles of R2d2 with high copy number (R2d2(HC)) in natural populations. We replicate this finding in multiple closed breeding populations from six outbred backgrounds segregating for R2d2 alleles. We find that R2d2(HC) rapidly increases in frequency, and in most cases becomes fixed in significantly fewer generations than can be explained by genetic drift. R2d2(HC) is also associated with significantly reduced litter sizes in heterozygous mothers, making it a true selfish allele. Our data provide direct evidence of populations actively undergoing selfish sweeps, and demonstrate that meiotic drive can rapidly alter the genomic landscape in favor of mutations with neutral or even negative effects on overall Darwinian fitness. Further study will reveal the incidence of selfish sweeps, and will elucidate the relative contributions of selfish genes, adaptation and genetic drift to evolution.

3-Dimensional histological reconstruction and imaging of the murine pancreas.

Visualization of important disease-driving tissues in their native morphological state, such as the pancreas, given its importance in glucose homeostasis and diabetes, provides critical insight into the etiology and progression of disease and our understanding of how cellular changes impact disease severity. Numerous challenges to maintaining tissue morphology exist when one attempts to preserve or to recreate such tissues for histological evaluation. We have overcome many of these challenges and have developed new methods for visualizing the whole murine pancreas and single islets of Langerhans in an effort to gain a better understanding of how islet cell volume, spatial distribution, and vascularization are altered as diabetes progresses. These methods are readily adaptable without requirement for costly specialized equipment, such as magnetic resonance imaging, positron emission tomography, or computed tomography, and can be used to provide additional robust analysis of diabetes susceptibility in mouse models of Type 1 and Type II diabetes.

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.

Inflammatory dilated cardiomyopathy in Abcg5-deficient mice.

Dilated cardiomyopathy (DCM) in A/J mice homozygous for the spontaneous thrombocytopenia and cardiomyopathy (trac) mutation results from a single base pair change in the Abcg5 gene. A similar mutation in humans causes sitosterolemia with high plant sterol levels, hypercholesterolemia, and early onset atherosclerosis. Analyses of CD3+ and Mac-3+ cells and stainable collagen in hearts showed inflammation and myocyte degeneration in A/J-trac/trac mice beginning postweaning and progressed to marked dilative and fibrosing cardiomyopathy by 140 days. Transmission electron microscopy (TEM) demonstrated myocyte vacuoles consistent with swollen endoplasmic reticulum (ER). Myocytes with cytoplasmic glycogen and irregular actinomyosin filament bundles formed mature intercalated disks with normal myocytes suggesting myocyte repair. A/J-trac/trac mice fed lifelong phytosterol-free diets did not develop cardiomyopathy. BALB/cByJ-trac/trac mice had lesser inflammatory infiltrates and later onset DCM. BALB/cByJ-trac/trac mice changed from normal to phytosterol-free diets had lesser T cell infiltrates but persistent monocyte infiltrates and equivalent fibrosis to mice on normal diets. B- and T-cell-deficient BALB/cBy-Rag1(null) trac/trac mice fed normal diets did not develop inflammatory infiltrates or DCM. We conclude that the trac/trac mouse has many features of inflammatory DCM and that the reversibility of myocardial T cell infiltration provides a novel model for investigating the progression of myocardial fibrosis.

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.

Genome-wide identification of mouse congenital heart disease loci.

Empirical evidence supporting a genetic basis for the etiology of congenital heart disease (CHD) is limited and few disease-causing mutations have been identified. To identify novel CHD genes, we performed a forward genetic screen to identify mutant mouse lines with heritable CHD. Lines with recessive N-ethyl-N-nitrsourea-induced CHD-causing mutations were identified using a three-generation backcross. A hierarchical screening protocol was used to test the hypothesis that the fetal-to-neonatal circulatory transition unmasks the specific structural heart defects observed in CHD. Mice with heart defects were efficiently ascertained by selecting for pups exhibiting perinatal lethality and characterizing their cardiac pathology. A marked increase of perinatal lethality was observed in the mutagen-treated cohort compared with an untreated backcross population. Cardiac pathology on perinatal lethals revealed cardiovascular defects in 79 pups from 47 of 321 mutagenized lines. All identified structural abnormalities were analogous to previously described forms of human CHD. Furthermore, the phenotypic recurrence and variance patterns across all lines were similar to human CHD prevalence and recurrence patterns. We mapped the locus responsible for heritable atrioventricular septal defects in six lines (avc1-6). Our screen demonstrated that 'sporadic' CHD may have major genetic component and established a practical, efficient approach for identifying CHD candidate genes.

The Diversity Outbred mouse population.

The Diversity Outbred (DO) population is a heterogeneous stock derived from the same eight founder strains as the Collaborative Cross (CC) inbred strains. Genetically heterogeneous DO mice display a broad range of phenotypes. Natural levels of heterozygosity provide genetic buffering and, as a result, DO mice are robust and breed well. Genetic mapping analysis in the DO presents new challenges and opportunities. Specialized algorithms are required to reconstruct haplotypes from high-density SNP array data. The eight founder haplotypes can be combined into 36 possible diplotypes, which must be accommodated in QTL mapping analysis. Population structure of the DO must be taken into account here. Estimated allele effects of eight founder haplotypes provide information that is not available in two-parent crosses and can dramatically reduce the number of candidate loci. Allele effects can also distinguish chance colocation of QTL from pleiotropy, which provides a basis for establishing causality in expression QTL studies. We recommended sample sizes of 200-800 mice for QTL mapping studies, larger than for traditional crosses. The CC inbred strains provide a resource for independent validation of DO mapping results. Genetic heterogeneity of the DO can provide a powerful advantage in our ability to generalize conclusions to other genetically diverse populations. Genetic diversity can also help to avoid the pitfall of identifying an idiosyncratic reaction that occurs only in a limited genetic context. Informatics tools and data resources associated with the CC, the DO, and their founder strains are developing rapidly. We anticipate a flood of new results to follow as our community begins to adopt and utilize these new genetic resource populations.

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.

Comparison of unrestrained plethysmography and forced oscillation for identifying genetic variability of airway responsiveness in inbred mice.

Lung function detection in mice is currently most accurately measured by invasive techniques, which are costly, labor intensive, and terminal. This limits their use for large-scale or longitudinal studies. Noninvasive assays are often used instead, but their accuracy for measuring lung function parameters such as resistance and elastance has been questioned in studies involving small numbers of mouse strains. Here we compared parameters detected by two different methods using 29 inbred mouse strains: enhanced pause (Penh), detected by unrestrained plethysmography, and central airway resistance and lung elastance, detected by a forced oscillation technique. We further tested whether the phenotypic variations were determined by the same genomic location in genome-wide association studies using a linear mixed model algorithm. Penh, resistance, and elastance were measured in nonexposed mice or mice exposed to saline and increasing doses of aerosolized methacholine. Because Penh differed from airway resistance in several strains and because the peak genetic associations found for Penh, resistance, or elastance were located at different genomic regions, we conclude that using Penh as an indicator for lung function changes in high-throughput genetic studies (i.e., genome-wide association studies or quantitative trait locus studies) measures something fundamentally different than airway resistance and lung elastance.

Stochastic variation of transcript abundance in C57BL/6J mice.

BACKGROUND: Transcripts can exhibit significant variation in tissue samples from inbred laboratory mice. We have designed and carried out a microarray experiment to examine transcript variation across samples from adipose, heart, kidney, and liver tissues of C57BL/6J mice and to partition variation into within-mouse and between-mouse components. Within-mouse variance captures variation due to heterogeneity of gene expression within tissues, RNA-extraction, and array processing. Between-mouse variance reflects differences in transcript abundance between genetically identical mice. RESULTS: The nature and extent of transcript variation differs across tissues. Adipose has the largest total variance and the largest within-mouse variance. Liver has the smallest total variance, but it has the most between-mouse variance. Genes with high variability can be classified into groups with correlated patterns of expression that are enriched for specific biological functions. Variation between mice is associated with circadian rhythm, growth hormone signaling, immune response, androgen regulation, lipid metabolism, and the extracellular matrix. Genes showing correlated patterns of within-mouse variation are also associated with biological functions that largely reflect heterogeneity of cell types within tissues. CONCLUSIONS: Genetically identical mice can experience different individual outcomes for medically important traits. Variation in gene expression observed between genetically identical mice can identify functional classes of genes that are likely to vary in the absence of experimental perturbations, can inform experimental design decisions, and provides a baseline for the interpretation of gene expression data in interventional studies. The extent of transcript variation among genetically identical mice underscores the importance of stochastic and micro-environmental factors and their phenotypic consequences.