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

Abstract

Background: Age-related gut microbial changes have been widely investigated over the past decade. Most of the previous age-related microbiome studies were conducted on the Western population, and the short-read sequencing (e.g., 16S V4 or V3-V4 region) was the most common microbiota profiling method. We evaluated the gut compositional differences using the long-read sequencing approach (i.e., PacBio sequencing targeting the full-length V1-V9 regions) to enable a deeper taxonomic resolution and better characterize the gut microbiome of Singaporeans from different age groups.

Results: A total of 83 research participants were included in this study. Although no significant differences were detected in alpha and beta diversity, our study demonstrated several bacterial taxa with abundances that were significantly different across age groups. With young individuals as the reference group, Eggerthella lenta and Bacteroides uniformis were found to be significantly altered in the middle-aged group, while Catenibacterium mitsuokai and Bacteroides plebeius were significantly altered in the elderly group. These age-related differences in the gut microbiome were associated with aberrations in several predicted functional pathways, including dysregulations of pathways related to lipopolysaccharide and tricarboxylic acid cycle in older adults.

Conclusions: The utilization of long-read sequencing facilitated the identification of species- and strain-level differences across age groups, which was challenging with the partial 16S rRNA sequencing approach. Nevertheless, replication studies are warranted to confirm our findings, and if confirmed, further in vitro and in vivo studies are crucial to better understand the impact of the altered levels of age-related bacterial taxa. Additionally, the modest performance of strain-level taxonomic classification using 16S-ITS-23S gene sequences, likely due to the limited depth of currently available alignment databases, highlights the need for optimization and refinement in curating these databases for the long-read sequencing approach.