永西 紗耶香

ABSTRACT Aims Alzheimer's disease (AD) is a leading cause of dementia, with increasing prevalence. Mutations in genes like MAPT, PSEN1, and PSEN2 are risk factors, leading to the development of several AD model mice. Recent hypotheses suggest AD brain pathology involves abnormal neurodevelopment, decreased pH, and neural hyperexcitation. However, it remains unclear to what extent these pathologies are reflected in the gene expression changes of AD models. This study aims to compare gene expression patterns in the brains of multiple AD model mice with those related to these three factors, evaluating the extent of overlap. Methods We conducted a comprehensive search of public databases, collecting 20 gene expression datasets from the hippocampus of AD model mice. These datasets were compared with gene sets related to hippocampal maturation, brain pH, and neural hyperexcitation to statistically assess overlap. Pathway enrichment analysis explored the biological relevance of these gene expression changes. Results The extent of overlap with maturity‐, pH‐, and hyperexcitation‐associated genes varied across AD models, showing significant correlations between lower maturity, lower pH, and increased neural hyperexcitation. In MAPT mutant and APP+PSEN1 homozygous transgenic mice, these signatures became more pronounced with age. Pathway meta‐analysis revealed that genes associated with maturity, pH, and hyperexcitation in AD models are involved in synaptic and channel functions, as well as inflammatory responses, consistent with previous studies. Conclusion These findings suggest that pathophysiological changes related to maturity, pH, and neural hyperexcitation play varying roles across individual AD model mice. Our recent study found a negative correlation between disease progression and actual pH levels in human AD patients. Considering the results presented in this study, maturity and neural hyperexcitation, which are correlated with pH, may also be linked to disease progression. Thus, gene expression changes in these factors could be useful markers for assessing the pathology in AD models.