Abstract The greatest risk factor for cognitive decline is aging. The biological mechanisms for this decline remain enigmatic due, in part, to the confounding of normal aging mechanisms and those that contribute to cognitive impairment. Importantly, many individuals exhibit impaired cognition in age, while some retain functionality despite their age. Here, we establish a behavioral testing paradigm to characterize age-related cognitive heterogeneity in inbred aged C57BL/6 mice and reliably separate animals into cognitively “intact” (resilient) and “impaired” subgroups using a high-resolution home-cage testing paradigm for spatial discrimination. RNA sequencing and subsequent pathway analyses of cognitively stratified mice revealed molecular signatures unique to cognitively impaired animals, including transcriptional down-regulation of genes involved in mitochondrial oxidative phosphorylation (OXPHOS) and sirtuin (Sirt1 and Sirt3) expression in the hippocampus. Mitochondrial function assessed using high-resolution respirometry indicated a reduced OXPHOS coupling efficiency in cognitively impaired animals with subsequent hippocampal analyses revealing an increase in the oxidative damage marker (3-nitrotyrosine) and an up-regulation of antioxidant enzymes (Sod2, Sod1, Prdx6, etc.). Aged–impaired animals also showed increased levels of IL-6 and TNF-α gene expression in the hippocampus and increased serum levels of proinflammatory cytokines, including IL-6. These results provide critical insight into the diversity of brain aging in inbred animals and reveal the unique mechanisms that separate cognitive resilience from cognitive impairment. Our data indicate the importance of cognitive stratification of aging animals to delineate the mechanisms underlying cognitive impairment and test the efficacy of therapeutic interventions.
[1]
Z. Ungvari,et al.
Simultaneous assessment of cognitive function, circadian rhythm, and spontaneous activity in aging mice
,
2018,
GeroScience.
[2]
Jaak Vilo,et al.
ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap
,
2015,
Nucleic Acids Res..
[3]
A. Smit,et al.
Sheltering Behavior and Locomotor Activity in 11 Genetically Diverse Common Inbred Mouse Strains Using Home-Cage Monitoring
,
2014,
PloS one.
[4]
Michael Kinter,et al.
A Quantitative Proteomic Profile of the Nrf2-Mediated Antioxidant Response of Macrophages to Oxidized LDL Determined by Multiplexed Selected Reaction Monitoring
,
2012,
PloS one.
[5]
B. Spruijt,et al.
High‐throughput phenotyping of avoidance learning in mice discriminates different genotypes and identifies a novel gene
,
2012,
Genes, brain, and behavior.
[6]
S. Rauser,et al.
Normalization in MALDI-TOF imaging datasets of proteins: practical considerations
,
2011,
Analytical and bioanalytical chemistry.
[7]
George M. Nixon,et al.
Practical Considerations of
,
1945
.