calls by the IMPC, the likelihood is that it represents strain background effects, an issue that reinforces the need for careful and detailed phenotyping on multiple backgrounds in order to find useful models.
From Human Polymorphism to Mouse Models
Some from the clinical genetics community, seeing successes from genome‐wide association studies (GWAS), claim that with modern genetics and next generation sequencing “the model for the human is now the human” and the use of other organisms is redundant. However, when combined with human genetic studies, such as GWAS for complex genetic diseases, it is now possible to reverse engineer mouse models from development toward validation of the human discoveries. This has been ongoing for many years using traditional recombineering approaches. For example, mutations in the Abcc6, ATP‐binding cassette, sub‐family C (CFTR/MRP), member and member 6 (ABCC6) gene were identified by several groups to cause pseudoxanthoma elasticum (PXE) in humans, a systemic disease characterized by ectopic mineralization. Two groups made null mutations of this gene in mice that recapitulated many of the phenotypes seen in humans [77, 78]. Many other genes produce genocopies of ectopic mineralization, and the discovery of four inbred strains with the same spontaneous Abcc6 hypomorphic allele but very different severity of PXE lesions [79] revealed many modifier genes in the mouse and by extension in humans [42].
Complex human genetic diseases, such as psoriasis, have been difficult to model in mice [80]. However, GWAS studies identified several single nucleotide polymorphisms in the caspase recruitment domain family, member 14 (CARD14) gene [81, 82]. One of these single nucleotide polymorphism (SNPs) was reproduced by three different laboratories in mice, all of which recapitulated the skin disease [83–85]. One lab made the second allelic mutation, which did not have any effect [85]. These are a few of many examples where genetic engineering in mice can either validate or fail to match molecular discoveries in human patient cohorts.
New Mouse Genetic Tools
The power of mouse genetics is being increasingly exploited, for example by using the resources of inbred strains which are now being used to construct genetic reference populations, such as the Collaborative Cross [86], which mimics the genetic complexity of human populations and the Diversity Outcross mice, a stock of truly outbred mice derived from the incipient inbred collaborative cross strains [87], that can provide defined heterozygosity. Together with the ability to manipulate the genome through reverse genetics, these novel mouse genetic tools provide complementarity to human clinical and genetic studies never before available. Medicine will continue to face major challenges for which mouse models will prove to be invaluable; for example, the ability to predict whether a particular individual will develop one or more of hundreds of different diseases for which they carry predisposing alleles, the accurate prediction of risk or prognosis or a particular disease in an individual and the prioritization of the many candidate genes arising from GWAS studies [88–90]. Success in translational research depends on the combined efforts of investigators in the laboratory and clinic to create the ideal slippers (mice) for Cinderella to go to the Ball.
Patient Derived Xenograft (PDX) Models
Mouse cancer models, be they spontaneous, induced by a variety of means, or genetically engineered, have been invaluable as tools to dissect underlying mechanisms of disease. However, they have had a smaller impact on translational studies to directly impact cancer treatment in humans. Development of refined immunodeficient mouse models [64, 91] has enabled engraftment of human cancer cells into these mice providing a tool to directly assess the human cancers in this xenograft model. These patient derived xenografts (PDX models) are used for target‐validation and drug‐assessment studies [92].
Acknowledgments
The authors thank Zoe Reifsneider for her help in preparing the graphics. This work was supported in part by grants from the National Institutes of Health (R13 OD010920, to JPS and JMW; R01‐CA089713 and P30‐CA034196 to JPS) and the European Union (Teacher, Erasmus+ Programme of the Commission of the European Union H2020, 600803‐EPP‐1‐2018‐1‐ES‐EPPKA2; to JPS, PNS, and PV).
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