Gut microbiota dynamics in SAMP8 mice: insights from machine learning and longitudinal analysis

Area of Science:

• Microbiology
• Gerontology
• Bioinformatics

Background:

• Gut microbiota composition changes significantly with age.
• The SAMP8 mouse model exhibits accelerated aging, making it valuable for studying age-related changes.
• Longitudinal studies on gut microbiota dynamics in aging SAMP8 mice are limited.

Purpose of the Study:

• To comprehensively analyze gut microbiota dynamics during aging in SAMP8 mice.
• To identify age-related shifts in microbial diversity, composition, and trajectories.
• To discover potential microbial biomarkers for aging.

Main Methods:

• 16S rRNA sequencing was used to profile gut microbiota in SAMP8 mice at 1, 3, 7, and 10 months.
• Alpha and beta diversity analyses were performed to assess changes in microbial community structure.
• Random Forest analysis was employed for biomarker discovery by comparing young and aged groups.

Main Results:

• Alpha diversity (Shannon index) significantly decreased with age.
• Beta diversity showed distinct clustering between young and aged mice.
• Key bacterial phyla like Firmicutes and Actinobacteria declined, while Proteobacteria and Bacteroidetes increased.
• Peptococcus was identified as a potential aging biomarker with high diagnostic accuracy (AUC = 0.78).

Conclusions:

• Gut microbiota undergoes significant restructuring during aging in SAMP8 mice.
• Specific bacterial taxa, including Peptococcus, can serve as reliable biomarkers for aging.
• These findings provide insights into microbiota-aging interactions and potential targets for interventions.