Bioinformatics Analysis and Experimental Verification of Exercise for Aging Mice in Different Brain Regions Based on Transcriptome Sequencing

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Abstract

Purpose: Physical exercise mitigates the effects of aging and cognitive decline. However, the precise neurobiological mechanisms underlying this phenomenon remain unclear. The primary aim of this study was to investigate the protective effect of exercise on age-related memory deficits in the prefrontal cortex (PFC) and hippocampus using bioinformatic analysis and biochemical verification. Methods: Young and aging mice were subjected to natural feeding or treadmill exercise (12 m/min, 8 weeks). Cognitive function was accessed using the Barnes maze and novel object recognition. Bioinformatic analysis was performed to identify co-expressed genes in different groups and brain regions. The selected genes and pathways were validated using RT-qPCR. Results: Regular exercise significantly ameliorated age-related cognitive deficits. Four up-regulated targets (Ifi27l2a, Irf7, Oas1b, Ifit1) and one down-regulation (Septin2) were reversed by exercise, demonstrating the underlying mechanisms of cognitive functions induced by aging with exercise in the hippocampus and PFC. The Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses indicated that the NOD-like receptor signaling pathway was inhibited in the neuroinflammation effects of exercise in aging mice in both brain regions. Conclusion: Exercise enhances age-related learning and memory deficits. This beneficial effect may be attributed to the changes in five up/down-regulated genes and the NOD-like receptor signaling pathway in both the hippocampus and PFC. These findings establish the modulation of neuroinflammation as a pivotal molecular mechanism supporting exercise intervention in the brain aging process.

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Jin, Y., Wei, C., Huang, X., Zhang, D., Zhang, L., & Li, X. (2023). Bioinformatics Analysis and Experimental Verification of Exercise for Aging Mice in Different Brain Regions Based on Transcriptome Sequencing. Life, 13(10). https://doi.org/10.3390/life13101988

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