Multiomic Approach to Analyze Infant Gut Microbiota: Experimental and Analytical Method Optimization.

Area of Science:

• Microbiology
• Metabolomics
• Human Health

Background:

• The human intestinal microbiome is crucial for health, particularly in infants.
• 16S rRNA sequencing profiles gut microbiota, but multiomic approaches offer greater accuracy.
• This study optimized bacterial diversity analysis using 16S rRNA sequencing and metabolomics.

Purpose of the Study:

• To optimize a 16S rRNA amplicon sequencing workflow for bacterial diversity analysis in infant fecal samples.
• To validate the optimized workflow using metabolomic analysis.
• To investigate the impact of feeding type on infant gut microbiota composition and metabolic profiles.

Main Methods:

• Tested 48 analytical combinations for 16S rRNA sequencing and taxonomy assignment.
• Analyzed 18 infant fecal samples based on feeding type.
• Employed a multiomic approach combining 16S rRNA sequencing and metabolomics.

Main Results:

• The combination of V4 region sequencing, ASV identification, and VSEARCH for taxonomy assignment yielded the most accurate results.
• The optimized workflow detected significant differences in fecal samples based on feeding type.
• These microbial differences correlated with observed changes in the fecal metabolic profile.

Conclusions:

• A multiomic approach effectively validated the optimized 16S rRNA sequencing workflow.
• The study highlights the effectiveness of the proposed method for analyzing infant gut microbiota.
• Feeding type significantly influences infant gut microbial composition and metabolic activity.