Identification of age-associated microbial changes via long-read 16S sequencing

Area of Science:

• Microbiome research
• Aging biology
• Genomics

Background:

• Previous age-related microbiome studies primarily focused on Western populations using short-read sequencing.
• Short-read sequencing methods (e.g., 16S V4 or V3-V4 regions) offer limited taxonomic resolution.
• Characterizing the gut microbiome in diverse populations like Singaporeans requires advanced sequencing techniques.

Purpose of the Study:

• To evaluate gut compositional differences in Singaporeans across various age groups using long-read sequencing.
• To achieve deeper taxonomic resolution and better characterize age-related gut microbiome variations.
• To identify specific bacterial taxa and functional pathways associated with aging in the gut microbiome.

Main Methods:

• Utilized PacBio long-read sequencing targeting full-length V1-V9 regions of the 16S rRNA gene.
• Analyzed gut microbiome composition in 83 participants across different age groups.
• Employed bioinformatics to assess bacterial taxa abundance and predicted functional pathways.

Main Results:

• No significant differences in alpha and beta diversity were observed across age groups.
• Identified significant alterations in specific bacterial taxa: Eggerthella lenta and Bacteroides uniformis in the middle-aged group, and Catenibacterium mitsuokai and Bacteroides plebeius in the elderly group, compared to young individuals.
• Observed age-related microbiome differences associated with dysregulated predicted functional pathways, including those related to lipopolysaccharide and the tricarboxylic acid cycle in older adults.

Conclusions:

• Long-read sequencing enables species- and strain-level microbiome analysis, surpassing limitations of partial 16S rRNA sequencing.
• Findings highlight specific age-associated gut bacteria and functional pathway aberrations in Singaporeans.
• Further research, including replication studies and in vitro/in vivo investigations, is needed to validate findings and understand the functional impact of these age-related microbial shifts. Optimization of databases for strain-level classification using long-read sequencing is also recommended.