High-throughput mouse phenomics for characterizing mammalian gene function

The Unified Phenotype Ontology : a framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

Methods for Modeling Early Life Stress in Rodents

Animal models of early life stress/adversity (ELS) have provided a foundation from which our understanding of the psychoneuroimmunology of childhood trauma has expanded over recent decades. Rodent models are a cornerstone of the ELS literature with many studies utilizing paradigms based on early life separation/deprivation protocols and manipulating the cage environment. However, no animal model is perfect. In particular, the lack of standardization across ELS models has led to inconsistent results and raised questions regarding the translational value of common preclinical models. In this chapter, we present an overview of the history of ELS rodent models and discuss considerations relevant to the ongoing efforts to both improve existing models and generate novel paradigms to meet the evolving needs of molecular- and mechanism-based ELS research.

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

Study of tree shrew biology and models: A booming and prosperous field for biomedical research

The tree shrew ( Tupaia belangeri) has long been proposed as a suitable alternative to non-human primates (NHPs) in biomedical and laboratory research due to its close evolutionary relationship with primates. In recent years, significant advances have facilitated tree shrew studies, including the determination of the tree shrew genome, genetic manipulation using spermatogonial stem cells, viral vector-mediated gene delivery, and mapping of the tree shrew brain atlas. However, the limited availability of tree shrews globally remains a substantial challenge in the field. Additionally, determining the key questions best answered using tree shrews constitutes another difficulty. Tree shrew models have historically been used to study hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, myopia, and psychosocial stress-induced depression, with more recent studies focusing on developing animal models for infectious and neurodegenerative diseases. Despite these efforts, the impact of tree shrew models has not yet matched that of rodent or NHP models in biomedical research. This review summarizes the prominent advancements in tree shrew research and reflects on the key biological questions addressed using this model. We emphasize that intensive dedication and robust international collaboration are essential for achieving breakthroughs in tree shrew studies. The use of tree shrews as a unique resource is expected to gain considerable attention with the application of advanced techniques and the development of viable animal models, meeting the increasing demands of life science and biomedical research.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

A Lipid Nanoparticle-Based Method for the Generation of Liver-Specific Knockout Mice

Knockout mice are useful tools that can provide information about the normal function of genes, including their biochemical, developmental, and physiological roles. One problem associated with the generation of knockout mice is that the loss of some genes of interest produces a lethal phenotype. Therefore, the use of conditioned knockout mice, in which genes are disrupted in specific organs, is essential for the elucidation of disease pathogenesis and the verification of drug targets. In general, conditional knockout mice are produced using the Cre/loxP system; however, the production of the large numbers of Cre/flox knockout and control mice required for analysis requires substantial time and effort. Here, we describe the generation of liver-specific conditional knockout mice via the introduction of lipid nanoparticles encapsulating Cre mRNA into the liver of floxed mice. This technique does not require the production of offspring by mating floxed mice and is therefore more convenient than the conventional method. The results presented here demonstrate that the LNP-based method enables liver-specific gene knockout in a short period of time.

EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways

Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. Our classification algorithms have wide applications in developmental biology and robustly identify signaling defects in evolutionarily distant species. Furthermore, using automated phenotyping in high-throughput drug screens, we show that EmbryoNet can resolve the mechanism of action of pharmaceutical substances. As part of this work, we freely provide more than 2 million images that were used to train and test EmbryoNet.

GIPC3 couples to MYO6 and PDZ domain proteins and shapes the hair cell apical region

GIPC3 has been implicated in auditory function. Initially localized to the cytoplasm of inner and outer hair cells of the cochlea, GIPC3 increasingly concentrated in cuticular plates and at cell junctions during postnatal development. Early postnatal Gipc3 KO/KO mice had mostly normal mechanotransduction currents, but had no auditory brainstem response at one month of age. Cuticular plates of Gipc3 KO/KO hair cells did not flatten during development as did those of controls; moreover, hair bundles were squeezed along the cochlear axis in mutant hair cells. Junctions between inner hair cells and adjacent inner phalangeal cells were also severely disrupted in Gipc3 KO/KO cochleas. GIPC3 bound directly to MYO6, and the loss of MYO6 led to altered distribution of GIPC3. Immunoaffinity purification of GIPC3 from chicken inner ear extracts identified co-precipitating proteins associated with adherens junctions, intermediate filament networks, and the cuticular plate. Several of immunoprecipitated proteins contained GIPC-family consensus PDZ binding motifs (PBMs), including MYO18A, which binds directly to the PDZ domain of GIPC3. We propose that GIPC3 and MYO6 couple to PBMs of cytoskeletal and cell-junction proteins to shape the cuticular plate.

Summary statement

The PDZ-domain protein GIPC3 couples the molecular motors MYO6 and MYO18A to actin cytoskeleton structures in hair cells. GIPC3 is necessary for shaping the hair cell’s cuticular plate and hence the arrangement of the stereocilia in the hair bundle.

The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease

The International Mouse Phenotyping Consortium (IMPC; https://www.mousephenotype.org/) web portal makes available curated, integrated and analysed knockout mouse phenotyping data generated by the IMPC project consisting of 85M data points and over 95,000 statistically significant phenotype hits mapped to human diseases. The IMPC portal delivers a substantial reference dataset that supports the enrichment of various domain-specific projects and databases, as well as the wider research and clinical community, where the IMPC genotype-phenotype knowledge contributes to the molecular diagnosis of patients affected by rare disorders. Data from 9,000 mouse lines and 750 000 images provides vital resources enabling the interpretation of the ignorome, and advancing our knowledge on mammalian gene function and the mechanisms underlying phenotypes associated with human diseases. The resource is widely integrated and the lines have been used in over 4,600 publications indicating the value of the data and the materials.

Mendelian gene identification through mouse embryo viability screening

BACKGROUND

The diagnostic rate of Mendelian disorders in sequencing studies continues to increase, along with the pace of novel disease gene discovery. However, variant interpretation in novel genes not currently associated with disease is particularly challenging and strategies combining gene functional evidence with approaches that evaluate the phenotypic similarities between patients and model organisms have proven successful. A full spectrum of intolerance to loss-of-function variation has been previously described, providing evidence that gene essentiality should not be considered as a simple and fixed binary property.

METHODS

Here we further dissected this spectrum by assessing the embryonic stage at which homozygous loss-of-function results in lethality in mice from the International Mouse Phenotyping Consortium, classifying the set of lethal genes into one of three windows of lethality: early, mid, or late gestation lethal. We studied the correlation between these windows of lethality and various gene features including expression across development, paralogy and constraint metrics together with human disease phenotypes. We explored a gene similarity approach for novel gene discovery and investigated unsolved cases from the 100,000 Genomes Project.

RESULTS

We found that genes in the early gestation lethal category have distinct characteristics and are enriched for genes linked with recessive forms of inherited metabolic disease. We identified several genes sharing multiple features with known biallelic forms of inborn errors of the metabolism and found signs of enrichment of biallelic predicted pathogenic variants among early gestation lethal genes in patients recruited under this disease category. We highlight two novel gene candidates with phenotypic overlap between the patients and the mouse knockouts.

CONCLUSIONS

Information on the developmental period at which embryonic lethality occurs in the knockout mouse may be used for novel disease gene discovery that helps to prioritise variants in unsolved rare disease cases.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Multivariate phenotype analysis enables genome-wide inference of mammalian gene function

The function of the majority of genes in the human and mouse genomes is unknown. Investigating and illuminating this dark genome is a major challenge for the biomedical sciences. The International Mouse Phenotyping Consortium (IMPC) is addressing this through the generation and broad-based phenotyping of a knockout (KO) mouse line for every protein-coding gene, producing a multidimensional data set that underlies a genome-wide annotation map from genes to phenotypes. Here, we develop a multivariate (MV) statistical approach and apply it to IMPC data comprising 148 phenotypes measured across 4,548 KO lines. There are 4,256 (1.4% of 302,997 observed data measurements) hits called by the univariate (UV) model analysing each phenotype separately, compared to 31,843 (10.5%) hits in the observed data results of the MV model, corresponding to an estimated 7.5-fold increase in power of the MV model relative to the UV model. One key property of the data set is its 55.0% rate of missingness, resulting from quality control filters and incomplete measurement of some KO lines. This raises the question of whether it is possible to infer perturbations at phenotype-gene pairs at which data are not available, i.e., to infer some in vivo effects using statistical analysis rather than experimentation. We demonstrate that, even at missing phenotypes, the MV model can detect perturbations with power comparable to the single-phenotype analysis, thereby filling in the complete gene-phenotype map with good sensitivity. A factor analysis of the MV model's fitted covariance structure identifies 20 clusters of phenotypes, with each cluster tending to be perturbed collectively. These factors cumulatively explain 75% of the KO-induced variation in the data and facilitate biological interpretation of perturbations. We also demonstrate that the MV approach strengthens the correspondence between IMPC phenotypes and existing gene annotation databases. Analysis of a subset of KO lines measured in replicate across multiple laboratories confirms that the MV model increases power with high replicability.

A Simple Evolutionary Model of Genetic Robustness After Gene Duplication

When a dispensable gene is duplicated (referred to the ancestral dispensability denoted by O⁺), genetic buffering and duplicate compensation together maintain the duplicate redundancy, whereas duplicate compensation is the only mechanism when an essential gene is duplicated (referred to the ancestral essentiality denoted by O^-). To investigate these evolutionary scenarios of genetic robustness, I formulated a simple mixture model for analyzing duplicate pairs with one of the following states: double dispensable (DD), semi-dispensable (one dispensable one essential, DE), or double essential (EE). This model was applied to the yeast duplicate pairs from a whole-genome duplication (WGD) occurred about 100 million years ago (mya), and the mouse duplicate pairs from a WGD occurred about more than 500 mya. Both case studies revealed that the proportion of essentiality for those duplicates with ancestral essentiality [P_E(O^-)] was much higher than that for those with ancestral dispensability [P_E(O⁺)]. While it was negligible in the yeast duplicate pairs, P_E(O⁺) (about 20%) was shown statistically significant in the mouse duplicate pairs. These findings, together, support the hypothesis that both sub-functionalization and neo-functionalization may play some roles after gene duplication, though the former may be much faster than the later.

How much do model organism phenotypes contribute to the computational identification of human disease genes?

Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

This article has an associated First Person interview with the first author of the paper.

Editor's choice: We investigated the use of model organism phenotypes in the computational identification of disease genes, identifying several data biases and concluding that mouse model phenotypes contribute most to computational disease gene identification.

Automated, high-dimensional evaluation of physiological aging and resilience in outbred mice

Behavior and physiology are essential readouts in many studies but have not benefited from the high-dimensional data revolution that has transformed molecular and cellular phenotyping. To address this, we developed an approach that combines commercially available automated phenotyping hardware with a systems biology analysis pipeline to generate a high-dimensional readout of mouse behavior/physiology, as well as intuitive and health-relevant summary statistics (resilience and biological age). We used this platform to longitudinally evaluate aging in hundreds of outbred mice across an age range from 3 months to 3.4 years. In contrast to the assumption that aging can only be measured at the limits of animal ability via challenge-based tasks, we observed widespread physiological and behavioral aging starting in early life. Using network connectivity analysis, we found that organism-level resilience exhibited an accelerating decline with age that was distinct from the trajectory of individual phenotypes. We developed a method, Combined Aging and Survival Prediction of Aging Rate (CASPAR), for jointly predicting chronological age and survival time and showed that the resulting model is able to predict both variables simultaneously, a behavior that is not captured by separate age and mortality prediction models. This study provides a uniquely high-resolution view of physiological aging in mice and demonstrates that systems-level analysis of physiology provides insights not captured by individual phenotypes. The approach described here allows aging, and other processes that affect behavior and physiology, to be studied with improved throughput, resolution, and phenotypic scope.

Progress towards completing the mutant mouse null resource

The generation of a comprehensive catalog of null alleles covering all protein-coding genes is the goal of the International Mouse Phenotyping Consortium. Over the past 20 years, significant progress has been made towards achieving this goal through the combined efforts of many large-scale programs that built an embryonic stem cell resource to generate knockout mice and more recently employed CRISPR/Cas9-based mutagenesis to delete critical regions predicted to result in frameshift mutations, thus, ablating gene function. The IMPC initiative builds on prior and ongoing work by individual research groups creating gene knockouts in the mouse. Here, we analyze the collective efforts focusing on the combined null allele resource resulting from strains developed by the research community and large-scale production programs. Based upon this pooled analysis, we examine the remaining fraction of protein-coding genes focusing on clearly defined mouse-human orthologs as the highest priority for completing the mutant mouse null resource. In summary, we find that there are less than 3400 mouse-human orthologs remaining in the genome without a targeted null allele that can be further prioritized to achieve our overall goal of the complete functional annotation of the protein-coding portion of a mammalian genome.

Extensive identification of genes involved in congenital and structural heart disorders and cardiomyopathy

Clinical presentation of congenital heart disease is heterogeneous, making identification of the disease-causing genes and their genetic pathways and mechanisms of action challenging. By using in vivo electrocardiography, transthoracic echocardiography and microcomputed tomography imaging to screen 3,894 single-gene-null mouse lines for structural and functional cardiac abnormalities, here we identify 705 lines with cardiac arrhythmia, myocardial hypertrophy and/or ventricular dilation. Among these 705 genes, 486 have not been previously associated with cardiac dysfunction in humans, and some of them represent variants of unknown relevance (VUR). Mice with mutations in Casz1, Dnajc18, Pde4dip, Rnf38 or Tmem161b genes show developmental cardiac structural abnormalities, with their human orthologs being categorized as VUR. Using UK Biobank data, we validate the importance of the DNAJC18 gene for cardiac homeostasis by showing that its loss of function is associated with altered left ventricular systolic function. Our results identify hundreds of previously unappreciated genes with potential function in congenital heart disease and suggest causal function of five VUR in congenital heart disease.

The social aspects of genome editing: publics as stakeholders, populations and participants in animal research

The application of genome editing to animal research connects to a wide variety of policy concerns and public conversations. We suggest focusing narrowly on public opinion of genome editing is to overlook the range of positions from which people are brought into relationships with animal research through these technologies. In this paper, we explore three key roles that publics are playing in the development of genome editing techniques applied to animals in biomedical research. First, publics are positioned by surveys and focus groups as stakeholders with opinions that matter to the development of research technologies. Learning lessons from controversies over genetically modified food in Europe, these methods are used to identify problems in science-society relations that need to be managed. Second, people are recruited into research projects through participating in biobanks and providing data, where their contributions are encouraged by appeals to the public good and maintained by public confidence. Thirdly, patients are increasingly taking positions within research governance, as lay reviewers on funding panels, where their expertise helps align research priorities and practices with public expectations of research. These plural publics do not easily aggregate into a simple or singular public opinion on genome editing. We conclude by suggesting more attention is needed to the multiple roles that different publics expect - and are expected - to play in the future development of genomic technologies.

Turning the Curve Into Straight: Phenogenetics of the Spine Morphology and Coordinate Maintenance in the Zebrafish

The vertebral column, or spine, provides mechanical support and determines body axis posture and motion. The most common malformation altering spine morphology and function is adolescent idiopathic scoliosis (AIS), a three-dimensional spinal deformity that affects approximately 4% of the population worldwide. Due to AIS genetic heterogenicity and the lack of suitable animal models for its study, the etiology of this condition remains unclear, thus limiting treatment options. We here review current advances in zebrafish phenogenetics concerning AIS-like models and highlight the recently discovered biological processes leading to spine malformations. First, we focus on gene functions and phenotypes controlling critical aspects of postembryonic aspects that prime in spine architecture development and straightening. Second, we summarize how primary cilia assembly and biomechanical stimulus transduction, cerebrospinal fluid components and flow driven by motile cilia have been implicated in the pathogenesis of AIS-like phenotypes. Third, we highlight the inflammatory responses associated with scoliosis. We finally discuss recent innovations and methodologies for morphometrically characterize and analyze the zebrafish spine. Ongoing phenotyping projects are expected to identify novel and unprecedented postembryonic gene functions controlling spine morphology and mutant models of AIS. Importantly, imaging and gene editing technologies are allowing deep phenotyping studies in the zebrafish, opening new experimental paradigms in the morphometric and three-dimensional assessment of spinal malformations. In the future, fully elucidating the phenogenetic underpinnings of AIS etiology in zebrafish and humans will undoubtedly lead to innovative pharmacological treatments against spinal deformities.

Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards

Completing the genotype-to-phenotype map requires rigorous measurement of the entire multivariate organismal phenotype. However, phenotyping on a large scale is not feasible for many kinds of traits, resulting in missing data that can also cause problems for comparative analyses and the assessment of evolutionary trends across species. Measuring the multivariate performance phenotype is especially logistically challenging, and our ability to predict several performance traits from a given morphology is consequently poor. We developed a machine learning model to accurately estimate multivariate performance data from morphology alone by training it on a dataset containing performance and morphology data from 68 lizard species. Our final, stacked model predicts missing performance data accurately at the level of the individual from simple morphological measures. This model performed exceptionally well, even for performance traits that were missing values for >90% of the sampled individuals. Furthermore, incorporating phylogeny did not improve model fit, indicating that the phenotypic data alone preserved sufficient information to predict the performance based on morphological information. This approach can both significantly increase our understanding of performance evolution and act as a bridge to incorporate performance into future work on phenomics.

An Integrated Approach to Studying Rare Neuromuscular Diseases Using Animal and Human Cell-Based Models

As sequencing technology improves, the identification of new disease-associated genes and new alleles of known genes is rapidly increasing our understanding of the genetic underpinnings of rare diseases, including neuromuscular diseases. However, precisely because these disorders are rare and often heterogeneous, they are difficult to study in patient populations. In parallel, our ability to engineer the genomes of model organisms, such as mice or rats, has gotten increasingly efficient through techniques such as CRISPR/Cas9 genome editing, allowing the creation of precision human disease models. Such in vivo model systems provide an efficient means for exploring disease mechanisms and identifying therapeutic strategies. Furthermore, animal models provide a platform for preclinical studies to test the efficacy of those strategies. Determining whether the same mechanisms are involved in the human disease and confirming relevant parameters for treatment ideally involves a human experimental system. One system currently being used is induced pluripotent stem cells (iPSCs), which can then be differentiated into the relevant cell type(s) for in vitro confirmation of disease mechanisms and variables such as target engagement. Here we provide a demonstration of these approaches using the example of tRNA-synthetase-associated inherited peripheral neuropathies, rare forms of Charcot-Marie-Tooth disease (CMT). Mouse models have led to a better understanding of both the genetic and cellular mechanisms underlying the disease. To determine if the mechanisms are similar in human cells, we will use genetically engineered iPSC-based models. This will allow comparisons of different CMT-associated GARS alleles in the same genetic background, reducing the variability found between patient samples and simplifying the availability of cell-based models for a rare disease. The necessity of integrating mouse and human models, strategies for accomplishing this integration, and the challenges of doing it at scale are discussed using recently published work detailing the cellular mechanisms underlying GARS-associated CMT as a framework.

The venous system of E14.5 mouse embryos-reference data and examples for diagnosing malformations in embryos with gene deletions

Approximately one-third of randomly produced knockout mouse lines produce homozygous offspring, which fail to survive the perinatal period. The majority of these die around or after embryonic day (E)14.5, presumably from cardiovascular insufficiency. For diagnosing structural abnormalities underlying death and diseases and for researching gene function, the phenotype of these individuals has to be analysed. This makes the creation of reference data, which define normal anatomy and normal variations the highest priority. While such data do exist for the heart and arteries, they are still missing for the venous system. Here we provide high-quality descriptive and metric information on the normal anatomy of the venous system of E14.5 embryos. Using high-resolution digital volume data and 3D models from 206 genetically normal embryos, bred on the C57BL/6N background, we present precise descriptive and metric information of the venous system as it presents itself in each of the six developmental stages of E14.5. The resulting data shed new light on the maturation and remodelling of the venous system at transition of embryo to foetal life and provide a reference that can be used for detecting venous abnormalities in mutants. To explore this capacity, we analysed the venous phenotype of embryos from 7 knockout lines (Atp11a, Morc2a, 1700067K01Rik, B9d2, Oaz1, Celf4 and Coro1c). Careful comparisons enabled the diagnosis of not only simple malformations, such as dual inferior vena cava, but also complex and subtle abnormalities, which would have escaped diagnosis in the absence of detailed, stage-specific referenced data.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

Genome engineering in rodents - status quo and perspectives

The introduction of the CRISPR-Cas9 system in 2013 has revolutionized experimental genetics in mice and rats. This commentary gives an overview on the use of CRISPR either for gene editing in the germline or for editing and beyond editing in somatic cells. Future perspectives are opened by emerging CRISPR technologies that could enable genome engineering at larger scale.

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease

Most current biomedical and protein research focuses only on a small proportion of genes, which results in a lost opportunity to identify new gene-disease associations and explore new opportunities for therapeutic intervention. The International Mouse Phenotyping Consortium (IMPC) focuses on elucidating gene function at scale for poorly characterized and/or under-studied genes. A key component of the IMPC initiative is the implementation of a broad phenotyping pipeline, which is facilitating the discovery of pleiotropy. Characterizing pleiotropy is essential to identify gene-disease associations, and it is of particular importance when elucidating the genetic causes of syndromic disorders. Here we show how the IMPC is effectively uncovering pleiotropy and how the new mouse models and gene function hypotheses generated by the IMPC are increasing our understanding of the mammalian genome, forming the basis of new research and identifying new gene-disease associations.

Ancestry analysis indicates two different sets of essential genes in eukaryotic model species

Essential genes are so-called because they are crucial for organism perpetuation. Those genes are usually related to essential functions to cellular metabolism or multicellular homeostasis. Deleterious alterations on essential genes produce a spectrum of phenotypes in multicellular organisms. The effects range from the impairment of the fertilization process, disruption of fetal development, to loss of reproductive capacity. Essential genes are described as more evolutionarily conserved than non-essential genes. However, there is no consensus about the relationship between gene essentiality and gene age. Here, we identified essential genes in five model eukaryotic species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) and estimate their evolutionary ancestry and their network properties. We observed that essential genes, on average, are older than other genes in all species investigated. The relationship of network properties and gene essentiality convey with previous findings, showing essential genes as important nodes in biological networks. As expected, we also observed that essential orthologs shared by the five species evaluated here are old. However, all the species evaluated here have a specific set of young essential genes not shared among them. Additionally, these two groups of essential genes are involved with distinct biological functions, suggesting two sets of essential genes: (i) a set of old essential genes common to all the evaluated species, regulating basic cellular functions, and (ii) a set of young essential genes exclusive to each species, which perform specific essential functions in each species.

Molecular Profiling of the Cardiac Conduction System: the Dawn of a New Era

PURPOSE OF REVIEW

Recent technological advances have led to an increased ability to define the gene expression profile of the cardiac conduction system (CCS). Here, we review the most salient studies to emerge in recent years and discuss existing gaps in our knowledge as well as future areas of investigation.

RECENT FINDINGS

Molecular profiling of the CCS spans several decades. However, the advent of high-throughput sequencing strategies has allowed for the discovery of unique transcriptional programs of the many diverse CCS cell types. The CCS, a diverse structure with significant inter- and intra-component cellular heterogeneity, is essential to the normal function of the heart. Progress in transcriptomic profiling has improved the resolution and depth of characterization of these unique and clinically relevant CCS cell types. Future studies leveraging this big data will play a crucial role in improving our understanding of CCS development and function as well as translating these findings into tangible translational tools for the improved detection, prevention, and treatment of cardiac arrhythmias.

Modeling regulatory network topology improves genome-wide analyses of complex human traits

Genome-wide association studies (GWAS) have cataloged many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non-coding regions. Integration of GWAS with gene regulatory networks addresses both issues by aggregating weak genetic signals within regulatory programs. Here we develop a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. Our method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations. Applying this method to 18 human traits and 38 regulatory networks shows that genetic signals of complex traits are often enriched in interconnections specific to trait-relevant cell types or tissues. Prioritizing variants within enriched networks identifies known and previously undescribed trait-associated genes revealing biological and therapeutic insights.

Skeletal screening IMPC/KOMP using μCT and computer automated cryohistology: Application to the Efna4 KO mouse line

The IMPC/KOMP program provides the opportunity to screen mice harboring well defined gene-inactivation mutations in a uniform genetic background. The program performs a global tissue phenotyping survey that includes skeletal x-rays and bone density measurements. Because of the relative insensitivity of the two screening tests for detecting variance in bone architecture, we initiated a secondary screen based on μCT and a cryohistolomorphological workflow that was performed on the femur and vertebral compartments on 220 randomly selected knockouts (KOs) and 36 control bone samples over a 2 1/2 year collection period provided by one of the production/phenotyping centers. The performance of the screening protocol was designed to balance throughput and cost versus sensitivity and informativeness such that the output would be of value to the skeletal biology community. Here we report the reliability of this screening protocol to establish criteria for control skeletal variance at the architectural, dynamic and cellular histomorphometric level. Unexpected properties of the control population include unusually high variance in BV/TV in male femurs and greater bone formation and bone turnover rates in the female femur and vertebral trabeculae bone compartments. However, the manner for maintaining bone formation differed between these two bone sites. The vertebral compartment relies on maintaining a greater number of bone forming surfaces while the femoral compartment utilized more matrix production per cell. The comparison of the architectural properties obtained by μCT and histomorphology revealed significant differences in values for BV/TV, Tb.Th and Tb.N which is attributable to sampling density of the two methods. However, as a screening tool, expressing the ratio of KO to the control line as obtained by either method was remarkably similar. It identified KOs with significant variance which led to a more detailed histological analysis. Our findings are exemplified by the Efna4 KO, in which a high BV/TV was identified by μCT and confirmed by histomorphometry in the femur but not in the vertebral compartment. Dynamic labeling showed a marked increase in BFR which was attributable to increased labeling surfaces. Cellular analysis confirmed partitioning of osteoblast to labeling surfaces and a marked decrease in osteoclastic activity on both labeling and quiescent surfaces. This pattern of increased bone modeling would not be expected based on prior studies of the Ephrin-Ephrin receptor signaling pathways between osteoblasts and osteoclasts. Overall, our findings underscore why unbiased screening is needed because it can reveal unknown or unanticipated genes that impact skeletal variation.

Mouse Quantitative Proteomics Knowledgebase: reference protein concentration ranges in 20 mouse tissues using 5000 quantitative proteomics assays

Laboratory mouse is the most used animal model in biological research, largely due to its high conserved synteny with human. Researchers use mice to answer various questions ranging from determining a pathological effect of knocked out/in gene to understanding drug metabolism. Our group developed >5000 quantitative targeted proteomics assays for 20 mouse tissues and determined the concentration ranges of a total of more than 1600 proteins using heavy labelled internal standards. We describe here MouseQuaPro; a knowledgebase that hosts this collection of carefully curated experimental data. The Web-based application includes protein concentrations from >700 mouse tissue samples from three common research strains, corresponding to more than 200k experimentally determined concentrations. The knowledgebase integrates the assay and protein concentration information with their human orthologs, functional and molecular annotations, biological pathways, related human diseases, and known gene expressions. At its core are the protein concentration ranges, which provide insights into (dis)similarities between tissues, strains, and sexes. MouseQuaPro implements advanced search as well as filtering functionalities with a simple interface and interactive visualization. This information-rich resource provides an initial map of protein absolute concentration in mouse tissues and allows guided design of proteomics phenotyping experiments. The knowledgebase is available at mousequapro.proteincentre.com. (Reviewer access username and password: mousequapro_reviewer1234567).

Establishment and application of information resource of mutant mice in RIKEN BioResource Research Center

Online databases are crucial infrastructures to facilitate the wide effective and efficient use of mouse mutant resources in life sciences. The number and types of mouse resources have been rapidly growing due to the development of genetic modification technology with associated information of genomic sequence and phenotypes. Therefore, data integration technologies to improve the findability, accessibility, interoperability, and reusability of mouse strain data becomes essential for mouse strain repositories. In 2020, the RIKEN BioResource Research Center released an integrated database of bioresources including, experimental mouse strains, Arabidopsis thaliana as a laboratory plant, cell lines, microorganisms, and genetic materials using Resource Description Framework-related technologies. The integrated database shows multiple advanced features for the dissemination of bioresource information. The current version of our online catalog of mouse strains which functions as a part of the integrated database of bioresources is available from search bars on the page of the Center (https://brc.riken.jp) and the Experimental Animal Division (https://mus.brc.riken.jp/) websites. The BioResource Research Center also released a genomic variation database of mouse strains established in Japan and Western Europe, MoG⁺ (https://molossinus.brc.riken.jp/mogplus/), and a database for phenotype-phenotype associations across the mouse phenome using data from the International Mouse Phenotyping Platform. In this review, we describe features of current version of databases related to mouse strain resources in RIKEN BioResource Research Center and discuss future views.

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.

Patients affected by osteoporosis frequently present with decreased BMD and increased fracture risk. Genes are known to control the onset and progression of bone diseases such as osteoporosis. Therefore, we aimed to identify osteoporosis-related genes using BMD measures obtained from a large pool of mutant mice genetically modified for deletion of individual genes (knockout mice). In a collaborative endeavor involving several research sites world-wide, we generated and phenotyped 3,823 knockout mice and identified 200 genes which regulated BMD. Of the 200 BMD genes, 141 genes were previously not known to affect BMD. The discovery and study of novel BMD genes will help to better understand the causes and therapeutic options for patients with low BMD. In the long run, this will improve the clinical management of osteoporosis.

System-level analyses of keystone genes required for mammalian tooth development

When a null mutation of a gene causes a complete developmental arrest, the gene is typically considered essential for life. Yet, in most cases, null mutations have more subtle effects on the phenotype. Here we used the phenotypic severity of mutations as a tool to examine system‐level dynamics of gene expression. We classify genes required for the normal development of the mouse molar into different categories that range from essential to subtle modification of the phenotype. Collectively, we call these the developmental keystone genes. Transcriptome profiling using microarray and RNAseq analyses of patterning stage mouse molars show highly elevated expression levels for genes essential for the progression of tooth development, a result reminiscent of essential genes in single‐cell organisms. Elevated expression levels of progression genes were also detected in developing rat molars, suggesting evolutionary conservation of this system‐level dynamics. Single‐cell RNAseq analyses of developing mouse molars reveal that even though the size of the expression domain, measured in the number of cells, is the main driver of organ‐level expression, progression genes show high cell‐level transcript abundances. Progression genes are also upregulated within their pathways, which themselves are highly expressed. In contrast, a high proportion of the genes required for normal tooth patterning are secreted ligands that are expressed in fewer cells than their receptors and intracellular components. Overall, even though expression patterns of individual genes can be highly different, conserved system‐level principles of gene expression can be detected using phenotypically defined gene categories.

The phenotypic severity of mutations on mouse teeth is used to classify genes.

Genes essential for the progression of odontogenesis are highly expressed at the organ and cell level.

Many of the genes required for normal patterning are locally expressed ligands.

A Non-invasive Digital Biomarker for the Detection of Rest Disturbances in the SOD1G93A Mouse Model of ALS

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease that affects both central and peripheral nervous system, leading to the degeneration of motor neurons, which eventually results in muscle atrophy, paralysis, and death. Sleep disturbances are common in patients with ALS, leading to even further deteriorated quality of life. Investigating methods to potentially assess sleep and rest disturbances in animal models of ALS is thus of crucial interest. We used an automated home cage monitoring system (DVC®) to capture irregular activity patterns that can potentially be associated with sleep and rest disturbances and thus to the progression of ALS in the SOD1G93A mouse model. DVC® enables non-intrusive 24/7 long term animal activity monitoring, which we assessed together with body weight decline and neuromuscular function deterioration measured by grid hanging and grip strength tests in male and female mice from 7 until 24 weeks of age. We show that as the ALS progresses over time in SOD1G93A mice, activity patterns start becoming irregular, especially during day time, with frequent activity bouts that are neither observed in control mice nor in SOD1G93A at a younger age. The increasing irregularities of activity pattern are quantitatively captured by designing a novel digital biomarker, referred to as Regularity Disruption Index (RDI). We show that RDI is a robust measure capable of detecting home cage activity patterns that could be related to rest/sleep-related disturbances during the disease progression. Moreover, the RDI rise during the early symptomatic stage parallels grid hanging and body weight decline. The non-intrusive long-term continuous monitoring of animal activity enabled by DVC® has been instrumental in discovering novel activity patterns potentially correlated, once validated, with sleep and rest disturbances in the SOD1G93A mouse model of the ALS disease.

Phenotyping in Mice Using Continuous Home Cage Monitoring and Ultrasonic Vocalization Recordings

Over the last century, the study of mouse behavior has uncovered insights into brain molecular mechanisms while revealing potential causes of many neurological disorders. To this end, researchers have widely exploited the use of mutant strains, including those generated in mutagenesis screens and those produced using increasingly sophisticated genome engineering technologies. It is now relatively easy to access mouse models carrying alleles that faithfully recapitulate changes found in human patients or bearing variants of genes that provide data on those genes' functions. Concurrent with these developments has been an appreciation of the limitations of some current testing platforms, especially those monitoring complex behaviors. Out-of-cage observational testing is useful in describing overt persistent phenotypes but risks missing sporadic or intermittent events. Furthermore, measuring the progression of a phenotype, potentially over many months, can be difficult while relying on assays that may be susceptible to changes in the testing environment. In recent years, there has also been increasing awareness that measurement of behaviors in isolation can be limiting, given that mice attempt to hide behavioral cues of vulnerability. To overcome these limitations, laboratory animal science is capitalizing on progress in data capture and processing expertise. Moreover, as additional recording modes become commonplace, ultrasonic vocalization recording is an appealing focus, as mice use vocalizations in various social contexts. Using video and audio technologies, we record the voluntary, unprovoked behaviors and vocalizations of mice in social groups. Adoption of these approaches is undoubtedly set to increase, as they capture the round-the-clock behavior of mouse strains. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Continuous recording of home cage activity using the Home Cage Analyzer (HCA) system Support Protocol: Subcutaneous insertion of a radio frequency identification microchip in the inguinal area Basic Protocol 2: Continuous recording of mouse ultrasonic vocalizations in the home cage.

Cyclin-dependent kinases and rare developmental disorders

Extensive studies in the past 30 years have established that cyclin-dependent kinases (CDKs) exert many diverse, important functions in a number of molecular and cellular processes that are at play during development. Not surprisingly, mutations affecting CDKs or their activating cyclin subunits have been involved in a variety of rare human developmental disorders. These recent findings are reviewed herein, giving a particular attention to the discovered mutations and their demonstrated or hypothesized functional consequences, which can account for pathological human phenotypes. The review highlights novel, important CDK or cyclin functions that were unveiled by their association with human disorders, and it discusses the shortcomings of mouse models to reveal some of these functions. It explains how human genetics can be used in combination with proteome-scale interaction databases to loom regulatory networks around CDKs and cyclins. Finally, it advocates the use of these networks to profile pathogenic CDK or cyclin variants, in order to gain knowledge on protein function and on pathogenic mechanisms.

The parotid secretory protein BPIFA2 is a salivary surfactant that affects lipopolysaccharide action

NEW FINDINGS

What is the central question of this study? The salivary protein BPIFA2 binds lipopolysaccharide, but its physiological function is not known. This study uses a new knockout mouse model to explore the physiological role of BPIFA2 in the oral cavity and systemic physiology. What is the main finding and its importance? BPIFA2 is a crucial surfactant in mouse saliva. In its absence, saliva exhibits the surface tension of water. Depletion of BPIFA2 affects salivary and ingested lipopolysaccharide and leads to systemic sequelae that include increased insulin secretion and metabolomic changes. These results suggest that the lipopolysaccharide-binding activity of BPIFA2 affects the activity of ingested lipopolysaccharide in the intestine and that BPIFA2 depletion causes mild metabolic endotoxaemia.

ABSTRACT

Saliva plays important roles in the mastication, swallowing and digestion of food, speech and lubrication of the oral mucosa, antimicrobial and anti-inflammatory activities, and the control of body temperature in grooming animals. The salivary protein BPIFA [BPI fold containing family A member 2; former names: parotid secretory protein (PSP), SPLUN2 and C20orf70] is related to lipid-binding and lipopolysaccharide (LPS)-binding proteins expressed in the mucosa. Indeed, BPIFA2 binds LPS, but the physiological role of BPIFA2 remains to be determined. To address this question, Bpifa2 knockout (Bpifa2^{tm1(KOMP)Vlcg} ) (KO) mice were phenotyped, with emphasis on the saliva and salivary glands. Stimulated whole saliva collected from KO mice was less able to spread on a hydrophobic surface than wild-type saliva, and the surface tension of KO saliva was close to that of water. These data suggest that BPIFA2 is a salivary surfactant that is mainly responsible for the low surface tension of mouse saliva. The reduced surfactant activity of KO saliva did not affect consumption of dry food or grooming, but saliva from KO mice contained less LPS than wild-type saliva. Indeed, mice lacking BPIFA2 responded to ingested LPS with an increased stool frequency, suggesting that BPIFA2 plays a role in the solubilization and activity of ingested LPS. Consistent with these findings, BPIFA2-depleted mice also showed increased insulin secretion and metabolomic changes that were consistent with a mild endotoxaemia. These results support the distal physiological function of a salivary protein and reinforce the connection between oral biology and systemic disease.

Semantic characterization of adverse outcome pathways

This study was undertaken to systematically assess the utilities and performance of ontology-based semantic analysis in adverse outcome pathway (AOP) research. With an increasing number of AOPs developed by scientific domain experts to organize toxicity information and facilitate chemical risk assessment, there is a pressing need for objective approaches to evaluate the biological coherence and quality of these AOPs. Powered by ontologies covering a wide range of biological domains, abundant phenotypic data annotated ontologically, and some sophisticated knowledge computing tools, semantic analysis has great potential in this area of application. With the events in the AOP-Wiki first annotated into logical definitions and then grouped into phenotypic profiles by individual AOPs, the coherence and quality of AOPs were assessed at several levels: paired key event relationships (KER), all possible event pair combinations within AOPs, and the phenotypic profiles of AOPs, genes, biological pathways, human diseases, and selected chemicals. The semantic similarities were assessed at all these levels based on a unified cross-species vertebrate phenotype ontology encompassing the logical definitions of AOP events as well as many other domain ontologies. A substantial number of KERs and AOPs in the AOP-Wiki were found to be semantically coherent. These same coherent AOPs also mapped to many more genes, pathways, and diseases biologically aligned with the intended chain of events therein leading to their respective adverse outcomes. Significantly, these findings imply that semantic analysis should also have utilities in developing future AOPs by selecting candidate events from either the existing AOP-Wiki events or a broader collection of ontology terms semantically similar to the molecular initiating events or adverse outcomes of interest. In addition, semantic analysis enabled AOP networks to be constructed at the level of phenotypic profiles based on similarities, complementing those based on event sharing by bringing genes, pathways, diseases, and chemicals into the networks too-thus greatly expanding the biological scope and our understanding of AOPs.

An atlas of evidence-based phenotypic associations across the mouse phenome

To date, reliable relationships between mammalian phenotypes, based on diagnostic test measurements, have not been reported on a large scale. The purpose of this study was to present a large mouse phenotype-phenotype relationships dataset as a reference resource, alongside detailed evaluation of the resource. We used bias-minimized comprehensive mouse phenotype data and applied association rule mining to a dataset consisting of only binary (normal and abnormal phenotypes) data to determine relationships among phenotypes. We present 3,686 evidence-based significant associations, comprising 345 phenotypes covering 60 biological systems (functions), and evaluate their characteristics in detail. To evaluate the relationships, we defined a set of phenotype-phenotype association pairs (PPAPs) as a module of phenotypic expression for each of the 345 phenotypes. By analyzing each PPAP, we identified phenotype sub-networks consisting of the largest numbers of phenotypes and distinct biological systems. Furthermore, using hierarchical clustering based on phenotype similarities among the 345 PPAPs, we identified seven community types within a putative phenome-wide association network. Moreover, to promote leverage of these data, we developed and published web-application tools. These mouse phenome-wide phenotype-phenotype association data reveal general principles of relationships among mammalian phenotypes and provide a reference resource for biomedical analyses.

Human and mouse essentiality screens as a resource for disease gene discovery

The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery.

High-mobility group nucleosomal binding domain 2 protects against microcephaly by maintaining global chromatin accessibility during corticogenesis

The surface area of the human cerebral cortex undergoes dramatic expansion during late fetal development, leading to cortical folding, an evolutionary feature not present in rodents. Microcephaly is a neurodevelopmental disorder defined by an abnormally small brain, and many gene mutations have been found to be associated with primary microcephaly. However, mouse models generated by ablating primary microcephaly-associated genes often fail to recapitulate the severe loss of cortical surface area observed in individuals with this pathology. Here, we show that a mouse model with deficient expression of high-mobility group nucleosomal binding domain 2 (HMGN2) manifests microcephaly with reduced cortical surface area and almost normal radial corticogenesis, with a pattern of incomplete penetrance. We revealed that altered cleavage plane and mitotic delay of ventricular radial glia may explain the rising ratio of intermediate progenitor cells to radial glia and the displacement of neural progenitor cells in microcephalic mutant mice. These led to decreased self-renewal of the radial glia and reduction in lateral expansion. Furthermore, we found that HMGN2 protected corticogenesis by maintaining global chromatin accessibility mainly at promoter regions, thereby ensuring the correct regulation of the transcriptome. Our findings underscore the importance of the regulation of chromatin structure in cortical development and highlight a mouse model with critical insights into the etiology of microcephaly.

[An optimized method for embedding undecalcified mouse tibias in plastic blocks]

OBJECTIVE

To optimize the method for embedding multiple undecalcified mouse tibias in plastic blocks, improve the efficiency and stability of plastic embedding and reduce the detachment rate of plastic slides.

METHODS

Thirty undecalcified tibias from 15 B6 mice were used for plastic embedding after calcein labeling, fixation, dehydration and infiltration. The tibias were embedded in cylindrical plastic blocks with a diameter of 4 mm. For each bone, the 1/4 proximal tibia was cut off, and the remaining 3/4 was used for re-embedding. Five bones were embedded in a single block with each bone standing closely on the surface of a flat plate. The samples were randomized into control and experimental groups in all the processes of embedding, sectioning and staining. In the 3 groups with modified embedment, flowing CO2 was added into the embedding solution, embedding solution was applied to the section surface, and the slides were heated at 95 ℃ for 15 min. The polymerization time, slide detachment rate, bone formation and osteoblast parameters were analyzed.

RESULTS

We prepared 6 plastic blocks, each containing 5 tibias, whose cross sections were on the same plane. The blocks were completely polymerized and suitable for sectioning. Flowing CO2 into the embedding solution reduced the polymerization time and increased the rate of complete polymerization. Application of the embedding solution on the section surface significantly reduced the detachment rate of the sections (P < 0.05) without affecting bone formation analysis (P > 0.05). Heating the slides significantly lowered the detachment rate of the sections (P < 0.05) without affecting osteoblast analysis (P > 0.05).

CONCLUSIONS

The optimized method allows effective embedding of multiple undecalcified mice tibias in the same block and can be an ideal method for histological analysis of undecalcified bones.

Costs of Implementing Quality in Research Practice

Using standardized guidelines in preclinical research has received increased interest in light of recent concerns about transparency in data reporting and apparent variation in data quality, as evidenced by irreproducibility of results. Although the costs associated with supporting quality through a quality management system are often obvious line items in laboratory budgets, the treatment of the costs associated with quality failure is often overlooked and difficult to quantify. Thus, general estimations of quality costs can be misleading and inaccurate, effectively undervaluing costs recovered by reducing quality defects. Here, we provide examples of quality costs in preclinical research and describe how we have addressed misconceptions of quality management implementation as only marginally beneficial and/or unduly burdensome. We provide two examples of implementing a quality management system (QMS) in preclinical experimental (animal) research environments - one in Europe, the German Mouse Clinic, having established ISO 9001 and the other in the United States, the University of Kentucky (UK), having established Good Laboratory Practice-compliant infrastructure. We present a summary of benefits to having an effective QMS, as may be useful in guiding discussions with funders or administrators to promote interest and investment in a QMS, which ultimately supports shared, mutually beneficial outcomes.

Using mouse genetics to understand human skeletal disease

Technological advances have enabled the study of the human genome in incredible detail with relative ease. However, our ability to interpret the functional significance of the millions of genetic variants present within each individual is limited. As a result, the confident assignment of disease-causing variant calls remains a significant challenge. Here we explore how mouse genetics can help address this deficit in functional genomic understanding. Underpinned by marked genetic correspondence, skeletal biology shows inter-species similarities which provide important opportunities to use data from mouse models to direct research into the genetic basis of skeletal pathophysiology. In this article we outline critical resources that may be used to establish genotype/phenotype relationships in skeletal tissue, identify genes with established skeletal effects and define the transcriptome of critical skeletal cell types. Finally, we outline how these mouse resources might be utilized to progress from a list of human sequence variants toward plausible gene candidates that contribute to skeletal disease.

Missense Mutations in the Human Nanophthalmos Gene TMEM98 Cause Retinal Defects in the Mouse

Purpose

We previously found a dominant mutation, Rwhs, causing white spots on the retina accompanied by retinal folds. Here we identify the mutant gene to be Tmem98. In humans, mutations in the orthologous gene cause nanophthalmos. We modeled these mutations in mice and characterized the mutant eye phenotypes of these and Rwhs.

Methods

The Rwhs mutation was identified to be a missense mutation in Tmem98 by genetic mapping and sequencing. The human TMEM98 nanophthalmos missense mutations were made in the mouse gene by CRISPR-Cas9. Eyes were examined by indirect ophthalmoscopy and the retinas imaged using a retinal camera. Electroretinography was used to study retinal function. Histology, immunohistochemistry, and electron microscopy techniques were used to study adult eyes.

Results

An I135T mutation of Tmem98 causes the dominant Rwhs phenotype and is perinatally lethal when homozygous. Two dominant missense mutations of TMEM98, A193P and H196P, are associated with human nanophthalmos. In the mouse these mutations cause recessive retinal defects similar to the Rwhs phenotype, either alone or in combination with each other, but do not cause nanophthalmos. The retinal folds did not affect retinal function as assessed by electroretinography. Within the folds there was accumulation of disorganized outer segment material as demonstrated by immunohistochemistry and electron microscopy, and macrophages had infiltrated into these regions.

Conclusions

Mutations in the mouse orthologue of the human nanophthalmos gene TMEM98 do not result in small eyes. Rather, there is localized disruption of the laminar structure of the photoreceptors.