We did not detect any glia or microglia activation in WT-APP (Fig. 3C and F) compared with WT-GFP (Fig. 3B and E), meaning that the neuroinflammation does not play a role in the memory deficit we observed
However, we identify a possible role of the alpha7 subunit in the normal aging process that should be further investigated
The hAPP-SLA transduction in DG did not induce a memory deficit in beta2 KO, meaning that the Abeta/beta2-nAChR interaction is required to drive the memory deficit in this model
In the NPR task (7 months p.i.), the GFP-beta2 spent significantly more time exploring the novel compartment (p = 0.003; Fig. 4A), as well as APP-beta2 (p = 0.017; Fig. 4B).
We observed intracellular Abeta staining in the polymorphic layer of the DG that was absent in GFP-beta2 (Fig. 8)
At 7 months p.i., both GFP-alpha7 (p = 0.9; Fig. 5A) and APP-alpha7 groups (p = 0.3; Fig. 5B) displayed a memory deficit in the NPR task.
The analysis of the total exploration time during the NOR task showed that GFP-alpha7 spent similar time exploring the familiar and the novel objects (p = 0.76; Fig. 5D), whereas APP-alpha7 spent significantly more time exploring the novel object compared with the familiar one (p = 0.01; Fig. 5E)
In the NOR task (15 months p.i.) as well, both GFP-alpha7 and APP-alpha7 displayed a cognitive impairment (Fig. 5DeE)
Immunofluorescence performed with VHH 31-1 on brain slices of APP-alpha7 mice also showed intracellular Abeta oligomers in the polymorphic layer (Fig. 8, arrows), whereas GFP-alpha7 mice did not show any Abeta oligomers
However, the analysis of the recognition percentage for the same set of data showed that GFP-WT spent significantly more time exploring the novel object compared with APP-WT (p = 0.0032; Fig. 2E), suggesting the presence of a memory deficit in the APP-WT group.
Hence, the transduction of hAPP-SLA in DG induces recognition memory deficits as supported by NPR and NOR tasks
Similarly, in the NOR task (15 months p.i.), both the GFP-beta2 and the APP-beta2 displayed higher exploration of the novel object (p < 0.0001 and p = 0.0001, respectively; Fig. 4C and D), with no differences between the 2 groups in the recognition index (p = 0.7; Fig. 4E), meaning that beta2 mice injected with hAPP-SLA did not exhibit the recognition memory deficit observed in APP-WT
The locomotor behavior during the NOR habituation phase was measured by the total distance traveled: APP-WT showed higher locomotor activity compared with GFP-WT (p = 0.0027; Fig. 2F)
We finally measured anxiety levels using the LDB paradigm. No differences were observed between both groups for the index of time spent in the lit compartment (p = 0.7) as well as for the number of transitions (p = 0.2; Fig. 2GeH)
In addition, viral transduction did not induce any neuroinflammation, as shown in WT noninjected mice (Fig. 3A and D)
The presence of oligomeric Abeta in APP-WT was confirmed with the antibody VHH 31-1, specific for oligomeric forms of Abeta (Lafaye et al., 2009)
Abeta was mainly found in the polymorphic layer of the DG (Fig. 6, arrows)
Abeta intracellular accumulation in DG polymorphic layer was also confirmed with the rat monoclonal 7H3D6 antibody, also specific for oligomeric Abeta (Kumar et al., 2013) (Fig. 7, arrow)
Noninjected mice spent the same amount of time exploring novel and familiar compartments during the test session (p = 0.6; Fig. 5C), confirming the presence of a constitutive recognition memory deficit in age-matched alpha7 KO mice
Because the alpha7 KO had a memory deficit independent of hAPP-SLA expression, we could not draw any conclusion on the role of alpha7 in the memory deficit observed
We then investigated the presence of Abeta aggregates in the hippocampus of APP-beta2 and APP-alpha7. In APP-beta2, a positive staining for Abeta oligomers using the VHH 31-1 antibody was found (Fig. 8, arrows)
Several studies showed that in AD animal models, the appearance of the cognitive deficits precedes plaque deposition (Casas et al., 2004; Gouras et al., 2000; Kumar et al., 2013; Wirths et al., 2004)
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If you find BEL Commons useful in your work, please consider citing: Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018). BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database, 2018(3), 1–11.