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Assessing microbiota composition in the context of aging.
Diana Campos-Iglesias1, José M P Freije1, Carlos López-Otín1
1Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Universidad de Oviedo, Oviedo, Spain; Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain; Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain.
Methods in Cell Biology
|February 1, 2024
Summary
Changes in the gut microbiome are linked to age-related diseases. This study presents a simple 16S rRNA sequencing method for analyzing mouse gut microbiota composition to improve research standardization.
Area of Science:
- Microbiology
- Gerontology
- Genomics
Background:
- The gut microbiome, a complex microbial community, significantly impacts host health.
- Alterations in gut microbial composition are associated with age-related diseases like cardiovascular and neurological disorders, type 2 diabetes, and obesity.
- Understanding the gut microbiota's role in aging is crucial for developing interventions.
Purpose of the Study:
- To present a standardized and simple method for analyzing gut microbiota composition.
- To facilitate reproducible microbiome analysis in mouse fecal samples.
- To address the need for best practices in microbiome research.
Main Methods:
- Utilized 16S rRNA next-generation sequencing for microbiota analysis.
- Focused on mouse fecal samples for practical application.
- Detailed a straightforward protocol to minimize experimental variation.
Main Results:
- A simple, standardized method for mouse fecal microbiota analysis using 16S rRNA sequencing was established.
- The method aims to reduce variability in microbiome studies.
- Provides a foundation for further research into gut microbiota and aging.
Conclusions:
- Standardized methods are essential for reliable gut microbiota research.
- This 16S rRNA sequencing approach offers a practical solution for analyzing mouse fecal samples.
- Improved methodology will advance our understanding of the gut microbiome's influence on aging and disease.
Keywords:
16S rRNA sequencingAgingAmplicon sequencingFecal DNA extractionGut microbiotaMicrobiomeMouse modelsMore Related Videos
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