. Enhancing face validity of mouse models of Alzheimer's disease with natural genetic variation. PLoS Genet. 2019 May;15(5):e1008155. Epub 2019 May 31 PubMed.

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  1. This study complements recent and past work showing that genetic background can significantly influence the onset, extent, and progression of amyloid-associated pathology in mouse models. To the best of my knowledge, this line of investigation started in 2003 with Bruce Lamb testing the impact of different inbred strains on amyloid pathology in his APP-YAC mice (Lehman et al., 2003). Prior to that, Karen Ashe had shown that inbred strain background could influence lethality in APP transgenic lines (Carlson et al., 1997). More recently, Catherine Kaczorowski had a beautiful paper using the BXD recombinant inbred panel to show that even hemizygosity for differing alleles was sufficient to dramatically alter cognitive, biochemical, and transcriptional consequences of the 5xFAD transgenic model (Dec 2018 news; Neuner et al., 2019). I think that this paper from Gareth Howell’s lab adds to this line of work in confirming that genetic background can dramatically influence the traits we attribute to our transgenes.

    I am less sure how I see the new genetically diverse models being used in future. There are practical reasons that mouse geneticists have shied away from using these wild-derived strains—as the paper indicates, it is hard to determine which cognitive outcomes are feasible when baseline behaviors vary so much from the inbred strains we're used to. As I see it, the strongest use for the new wild-derived backgrounds will be for genetic studies. I think that the transcriptional analyses are the most striking of the outcomes tested here, and may be the most informative measure for future work on the effects of genetic background. 

    These recent studies lead me to believe that we should move toward preclinical testing of drug candidates using models expressed on outbred strain backgrounds with more genetic variation between individuals. You'd have to have a substantial effect to reach significance with the added inter-animal variability of this approach. This strategy might help to winnow out at the preclinical stage therapeutic ideas that don't work across a heterogeneous genetic landscape.

    References:

    . Genetic background regulates beta-amyloid precursor protein processing and beta-amyloid deposition in the mouse. Hum Mol Genet. 2003 Nov 15;12(22):2949-56. Epub 2003 Sep 23 PubMed.

    . Genetic modification of the phenotypes produced by amyloid precursor protein overexpression in transgenic mice. Hum Mol Genet. 1997 Oct;6(11):1951-9. PubMed.

    . Harnessing Genetic Complexity to Enhance Translatability of Alzheimer's Disease Mouse Models: A Path toward Precision Medicine. Neuron. 2019 Feb 6;101(3):399-411.e5. Epub 2018 Dec 27 PubMed.

    View all comments by Joanna Jankowsky
  2. Mouse models for Alzheimer’s disease only partially recapitulate the complexity of the human disease. Indeed, despite their aggressive amyloid pathology, current mouse models develop neither neurofibrillary tangles nor neuronal loss, both major hallmarks of AD. This is an important obstacle for the translation of preclinical findings into potential clinical applications. Most of the preclinical research is carried out on the inbred mouse strain C57BL/6. These mice have a very limited numbers of genetic polymorphisms, even across several generations of breeding. On one hand, such a genetic homogeneity has helped researchers to reduce data variability and improve comparisons across different labs. However, genetic homogeneity in laboratory mice poorly matches with the human counterpart of AD, which is mostly sporadic and driven by both environment and combinations of polymorphic alleles.

    Thus, while we are learning a lot from mouse models of AD, unfortunately, this increasing knowledge may have limited impact on our understanding of the actual disease. Onos and colleagues tackled this important topic, backcrossing the most popular C57BL/6 AD models onto different wild mouse strains, characterized by a broader genetic variation. Not surprisingly, wild strains exhibited variable degrees of neuronal loss, severity of amyloid and vascular pathology and microglia activation. Interestingly, transcriptomic analysis highlighted the strain, but not the disease, as the major driver of the gene expression variability. This suggests that the disease-associated gene expression signature identified in the canonical C57BL/6 models may appear remarkably different in mice with a different genetic background.

    Nonetheless, certain genes were consistently affected across all the strains. Most of these genes are critically expressed in myeloid cells, suggesting that certain immune pathways are conserved under AD pathology regardless of the strain differences. Possibly, comparison of transcriptomic alterations amongst multiple strains, both inbred and outbred, may help discern strain-dependent biases, thus highlighting true molecular players in Alzheimer disease.

    View all comments by Simone Brioschi
  3. Lovely contribution to the small but rapidly growing literature on the major impact that genetic background effects have on AD progression and severity. Reductionist models are a great start, but testing against genetic variation (modifiers) is essential to improve translational relevance of models. Just two minor tweaks to Pat McCaffery's solid review and summary of the study in PLoS Genetics: 1. The strains that Drs. Onos, Howell, and team exploited are commonly referred to as wild strains, but at this point they have been inbred at The Jackson Laboratory for many generations—87 generations in the case of WSB/EiJ. This is a good thing—they are a now a wonderful and stable resource for this and many other studies. 2. The statement about "three times the number of SNPs...as B6" needs a bit of explanation. Members of an inbred strain will be as isogenic as monozygotic human twins. But by studying the effects of AD variants on three different strains, Onos and colleagues have added back a great deal of DNA variation. These three strains will differ at well over 35 million sequence variants—as much or more than the number of common sequence variants in human populations.

    View all comments by Robert W. Williams

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