rel="nofollow" href="https://doi.org/10.1534/genetics.120.303596">https://doi.org/10.1534/genetics.120.303596.
39 39 Stoye, J.P., Fenner, S., Greenoak, G.E. et al. (1988). Role of endogenous retroviruses as mutagens: the hairless mutation of mice. Cell 54 (3): 383–391.
40 40 Maddox, D.M., Hicks, W.L., Vollrath, D. et al. (2011). An ENU‐induced mutation in the Mertk gene (Mertknmf12) leads to a slow form of retinal degeneration. Invest. Ophthalmol. Vis. Sci. 52 (7): 4703–4709.
41 41 Theiler, K. and Varnum, D.S. (1981). Development of coloboma (Cm/+), a mutation with anterior lens adhesion. Anat. Embryol. (Berl). 162 (1): 121–126.
42 42 Hess, E.J., Jinnah, H.A., Kozak, C.A., and Wilson, M.C. (1992). Spontaneous locomotor hyperactivity in a mouse mutant with a deletion including the Snap gene on chromosome 2. J. Neurosci. 12 (7): 2865–2874.
43 43 Birling, M.C., Schaeffer, L., Andre, P. et al. (2017). Efficient and rapid generation of large genomic variants in rats and mice using CRISMERE. Sci. Rep. 7: 43331.
44 44 Wang, X., Ria, M., Kelmenson, P.M. et al. (2005). Positional identification of TNFSF4, encoding OX40 ligand, as a gene that influences atherosclerosis susceptibility. Nat. Genet. 37 (4): 365–372.
45 45 Broccoli, V., Boncinelli, E., and Wurst, W. (1999). The caudal limit of Otx2 expression positions the isthmic organizer. Nature 401 (6749): 164–168.
46 46 Rickert, R.C., Rajewsky, K., and Roes, J. (1995). Impairment of T‐cell‐dependent B‐cell responses and B‐1 cell development in CD19‐deficient mice. Nature 376 (6538): 352–355.
47 47 Larsson, N.G., Wang, J., Wilhelmsson, H. et al. (1998). Mitochondrial transcription factor A is necessary for mtDNA maintenance and embryogenesis in mice. Nat. Genet. 18 (3): 231–236.
48 48 Knowlton, M.N. and Smith, C.L. (2017). Naming CRISPR alleles: endonuclease‐mediated mutation nomenclature across species. Mamm. Genome 28 (7–8): 367–376.
49 49 Cao, W., Kayama, H., Chen, M.L. et al. (2017). The xenobiotic transporter Mdr1 enforces T cell homeostasis in the presence of intestinal bile acids. Immunity 47 (6): 1182–1196. e10.
50 50 Mizuno, S., Takami, K., Daitoku, Y. et al. (2015). Peri‐implantation lethality in mice carrying megabase‐scale deletion on 5qc3.3 is caused by Exoc1 null mutation. Sci. Rep. 5: 13632.
51 51 Friedel, R.H. and Soriano, P. (2010). Gene trap mutagenesis in the mouse. Methods Enzymol. 477: 243–269.
52 52 Fritz, J.M., Dong, M., Apsley, K.S. et al. (2014). Deficiency of the BiP cochaperone ERdj4 causes constitutive endoplasmic reticulum stress and metabolic defects. Mol. Biol. Cell 25 (4): 431–440.
53 53 The Jackson Laboratory. (2009). Large‐scale enhancer trap project to generate transgenic cre alleles by Ron Davis at Baylor University [Internet]. http://www.informatics.jax.org (accessed 04 March 2021).
54 54 Li, L., Liu, P., Sun, L. et al. (2016). PiggyBac transposon‐based polyadenylation‐signal trap for genome‐wide mutagenesis in mice. Sci. Rep. 6: 27788.
55 55 Elso, C.M., Chu, E.P., Alsayb, M.A. et al. (2015). Sleeping beauty transposon mutagenesis as a tool for gene discovery in the NOD mouse model of type 1 diabetes. G3 5 (12): 2903–2911.
56 56 Echelard, Y., Vassileva, G., and McMahon, A.P. (1994). Cis‐acting regulatory sequences governing Wnt‐1 expression in the developing mouse CNS. Development 120 (8): 2213–2224.
57 57 Lewandoski, M., Wassarman, K.M., and Martin, G.R. (1997). Zp3‐cre, a transgenic mouse line for the activation or inactivation of loxP‐flanked target genes specifically in the female germ line. Curr. Biol. 7 (2): 148–151.
4 Discovering and Validating Mouse Models of Human Diseases: The Cinderella Effect
John P. Sundberg, Jerrold M. Ward, Peter Vogel, and Paul N. Schofield
Introduction
Historically, animal models (regardless of species) of human diseases were identified and proposed empirically. Namely the best fit was based largely on clinical similarities between the human and animal disease (Figure 4.1). This concept of “best fit,” or the “Cinderella Effect,” [1] has evolved considerably with the advance of technology to include direct correlations at the level of the genetic variation found in humans with specific diseases.
However, access to advanced technology does not guarantee the identification and development of valid animal models. In fact, without critical observation and proper experimental design, misinterpretation of experiment results can derail the valuable contribution that might be obtained from the use of animal models. In a relatively recent example, an investigation on inflammation compared eight‐week‐old C57BL/6J male mice (one inbred strain, one sex, one age) with a heterogeneous group of outbred human patients up to 55 years of age with blunt trauma, burns, and bacterial endotoxin responses. The results showed consistency in the overall transcriptional response in humans, but major differences when compared to those seen in a mouse model, which led the authors to conclude that mouse models were without value [2]. This paper caused a sensation in lay media (http://www.nytimes.com/2013/02/12/science/testing‐of‐some‐deadly‐diseases‐on‐mice‐mislead‐report‐says.html?smid=pl‐share) as well as scientific circles. However, re‐evaluation of the data revealed important areas of similarity [3]. Because the transcriptome data used in that study were available in publicly accessible databases, other groups were able to reanalyze the data and showed that mouse and human comparative studies were valuable even in this model [3, 4]. This change in interpretation, still debated, reflects a fundamental experimental design flaw in the study that led to the misinterpretations, namely “comparing apples to oranges” [4] and whether mouse and human diseases can be compared or not. One human, or even one group of humans, is unlikely to accurately model all humans, which is the lesson learned from approaches to personalized medicine [5]. One should not be surprised to find that young laboratory mice of a single strain, a single sex (the genetic equivalent of a single individual or a group of identical twins), and exposed to experimental insults, does not mirror the inflammatory response of a heterogeneous human adult population of mixed ages and both sexes. It is a general point that multiple inbred strains or outbred strains need to be used when attempting to model human disease [6]. Therefore, careful comparison at the most basic levels are necessary to accurately create and validate animal models for human diseases. This applies to genetic‐based diseases as well. Definitions of human diseases include common, often characteristic lesions, as well as less frequent to rare lesions (subtypes of the disease). These can be conceptually presented as a Gaussian curve with each bar representing a subgroup of any particular disease (Figure 4.2). Inbred mouse strains essentially present as an infinite number of one individual with both sexes due to their genetic homogeneity. As such, each inbred strain carrying a defined mutation may represent a model of one subtype of the human disease not all subtypes. Some strains may exhibit the common or characteristic lesion while others do not.
Real examples of this were exemplified by creating a null mutation in the epidermal growth factor receptor gene (Egfrtm1Dwt). When this allele was moved by breeding onto other strains to create hybrid stocks, a variety of phenotypes were observed, ranging from embryonic lethality at 4–11.5 days post conception (DPC) to live births with runting (Figure 4.3) [7, 8]. While this is an interesting observation, the question is really how can these observations help us to better understand human diseases. An example of how this phenomenon can explain the human disease subtypes came via a spontaneous hypomorphic allele of the laminin gamma 2 gene (Lamc2jeb) in mice.