Progress towards completing the mutant mouse null resource

Mouse phenome database: curated data repository with interactive multi-population and multi-trait analyses

The Mouse Phenome Database continues to serve as a curated repository and analysis suite for measured attributes of members of diverse mouse populations. The repository includes annotation to community standard ontologies and guidelines, a database of allelic states for 657 mouse strains, a collection of protocols, and analysis tools for flexible, interactive, user directed analyses that increasingly integrates data across traits and populations. The database has grown from its initial focus on a standard set of inbred strains to include heterogeneous mouse populations such as the Diversity Outbred and mapping crosses and well as Collaborative Cross, Hybrid Mouse Diversity Panel, and recombinant inbred strains. Most recently the system has expanded to include data from the International Mouse Phenotyping Consortium. Collectively these data are accessible by API and provided with an interactive tool suite that enables users' persistent selection, storage, and operation on collections of measures. The tool suite allows basic analyses, advanced functions with dynamic visualization including multi-population meta-analysis, multivariate outlier detection, trait pattern matching, correlation analyses and other functions. The data resources and analysis suite provide users a flexible environment in which to explore the basis of phenotypic variation in health and disease across the lifespan.

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.

Essential genes: a cross-species perspective

Protein coding genes exhibit different degrees of intolerance to loss-of-function variation. The most intolerant genes, whose function is essential for cell or/and organism survival, inform on fundamental biological processes related to cell proliferation and organism development and provide a window on the molecular mechanisms of human disease. Here we present a brief overview of the resources and knowledge gathered around gene essentiality, from cancer cell lines to model organisms to human development. We outline the implications of using different sources of evidence and definitions to determine which genes are essential and highlight how information on the essentiality status of a gene can inform novel disease gene discovery and therapeutic target identification.

Spontaneous allelic variant in deafness-blindness gene Ush1g resulting in an expanded phenotype

Relationships between novel phenotypic behaviors and specific genetic alterations are often discovered using target-specific, directed mutagenesis or phenotypic selection following chemical mutagenesis. An alternative approach is to exploit deficiencies in DNA repair pathways that maintain genetic integrity in response to spontaneously induced damage. Mice deficient in the DNA glycosylase NEIL1 show elevated spontaneous mutations, which arise from translesion DNA synthesis past oxidatively induced base damage. Several litters of Neil1 knockout mice included animals that were distinguished by their backwards-walking behavior in open-field environments, while maintaining frantic forward movements in their home cage environment. Other phenotypic manifestations included swim test failures, head tilting and circling. Mapping of the mutation that conferred these behaviors showed the introduction of a stop codon at amino acid 4 of the Ush1g gene. Ush1gbw/bw null mice displayed auditory and vestibular defects that are commonly seen with mutations affecting inner-ear hair-cell function, including a complete lack of auditory brainstem responses and vestibular-evoked potentials. As in other Usher syndrome type I mutant mouse lines, hair cell phenotypes included disorganized and split hair bundles, as well as altered distribution of proteins for stereocilia that localize to the tips of row 1 or row 2. Disruption to the bundle and kinocilium displacement suggested that USH1G is essential for forming the hair cell's kinocilial links. Consistent with other Usher type 1 models, Ush1gbw/bw mice had no substantial retinal degeneration compared with Ush1gbw /+ controls. In contrast to previously described Ush1g alleles, this new allele provides the first knockout model for this gene.

De novo PAM generation to reach initially inaccessible target sites for base editing

Base editing by CRISPR crucially depends on the presence of a protospacer adjacent motif (PAM) at the correct distance from the editing site. Here, we present and validate an efficient one-shot approach termed 'inception' that expands the editing range. This is achieved by sequential, combinatorial base editing: de novo generated synonymous, non-synonymous or intronic PAM sites facilitate subsequent base editing at nucleotide positions that were initially inaccessible, further opening the targeting range of highly precise editing approaches. We demonstrate the applicability of the inception concept in medaka (Oryzias latipes) in three settings: loss of function, by introducing a pre-termination STOP codon in the open reading frame of oca2; locally confined multi-codon changes to generate allelic variants with different phenotypic severity in kcnh6a; and the removal of a splice acceptor site by targeting intronic sequences of rx3. Using sequentially acting base editors in the described combinatorial approach expands the number of accessible target sites by 65% on average. This allows the use of well-established tools with NGG PAM recognition for the establishment of thus far unreachable disease models, for hypomorphic allele studies and for efficient targeted mechanistic investigations in a precise and predictable manner.

Genetic and Molecular Quality Control of Genetically Engineered Mice

Genetically engineered mice are used as avatars to understand mammalian gene function and develop therapies for human disease. During genetic modification, unintended changes can occur, and these changes may result in misassigned gene-phenotype relationships leading to incorrect or incomplete experimental interpretations. The types of unintended changes that may occur depend on the allele type being made and the genetic engineering approach used. Here we broadly categorize allele types as deletions, insertions, base changes, and transgenes derived from engineered embryonic stem (ES) cells or edited mouse embryos. However, the methods we describe can be adapted to other allele types and engineering strategies. We describe the sources and consequ ences of common unintended changes and best practices for detecting both intended and unintended changes by screening and genetic and molecular quality control (QC) of chimeras, founders, and their progeny. Employing these practices, along with careful allele design and good colony management, will increase the chance that investigations using genetically engineered mice will produce high-quality reproducible results, to enable a robust understanding of gene function, human disease etiology, and therapeutic development.

Hunt for mammalian sleep-regulating genes

Genetics is one of the various approaches adopted to understand and control mammalian sleep. Reverse genetics, which is usually applied to analyze sleep in gene-deficient mice, has been the mainstream field of genetic studies on sleep for the past three decades and has revealed that various molecules, including orexin, are involved in sleep regulation. Recently, forward genetic studies in humans and mice have identified gene mutations responsible for heritable sleep abnormalities, such as SIK3, NALCN, DEC2, the neuropeptide S receptor, and β1 adrenergic receptor. Furthermore, the protein kinase A-SIK3 pathway was shown to represent the intracellular neural signaling for sleep need. Large-scale genome-wide analyses of human sleep have been conducted, and many gene loci associated with individual differences in sleep have been found. The development of genome-editing technology and gene transfer by an adeno-associated virus has updated and expanded the genetic studies on mammals. These efforts are expected to elucidate the mechanisms of sleep–wake regulation and develop new therapeutic interventions for sleep disorders.

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.

X-linked palindromic gene families 4930567H17Rik and Mageb5 are dispensable for male mouse fertility

Mammalian sex chromosomes are enriched for large, nearly-identical, palindromic sequences harboring genes expressed predominately in testicular germ cells. Discerning if individual palindrome-associated gene families are essential for male reproduction is difficult due to challenges in disrupting all copies of a gene family. Here we generate precise, independent, deletions to assess the reproductive roles of two X-linked palindromic gene families with spermatid-predominant expression, 4930567H17Rik and Mageb5. Sequence analyses reveals mouse 4930567H17Rik and Mageb5 are orthologs of human HSFX3 and MAGEB5, respectively, where 4930567H17Rik/HSFX3 is harbored in a palindrome in humans and mice, while Mageb5 is not. Additional sequence analyses show 4930567H17Rik and HSFX3 are rapidly diverging in rodents and primates, respectively. Mice lacking either 4930567H17Rik or Mageb5 gene families do not have detectable defects in male fertility, fecundity, spermatogenesis, or in gene regulation, but do show differences in sperm head morphology, suggesting a potential role in sperm function. We conclude that while all palindrome-associated gene families are not essential for male fertility, large palindromes influence the evolution of their associated gene families.

The mouse resource at National Resource Center for Mutant Mice

Mouse models are essential for dissecting disease mechanisms and defining potential drug targets. There are more than 18,500 mouse strains available for research communities in National Resource Center for Mutant Mice (NRCMM) of China, affiliated with Model Animal Research Center of Nanjing University and Gempharmatech Company. In 2019, Gempharmatech launched the Knockout All Project (KOAP) aiming to generate null mutants and gene floxed strains for all protein-coding genes in mouse genome within 5 years. So far, KOAP has generated 8,004 floxed strains and 9,769 KO (knockout) strains (updated to Oct, 2021). NRCMM also created hundreds of Cre transgenic lines, mutant knock-in models, immuno-deficient models, and humanized mouse models. As a member of the international mouse phenotyping consortium (IMPC), NRCMM provides comprehensive phenotyping services for mouse models. In summary, NRCMM will continue to support biomedical community with new mouse models as well as related services.