Microbiome Sequencing

Microbiome sequencing characterizes microbial communities using molecular diagnostic assays, principally 16S rRNA gene sequencing and shotgun metagenomic sequencing. ^[1] Metagenomic sequencing approaches provide an ever-increasing amount of protein sequence data and have been exploited to study the role of the gut microbiota in human diseases and aging. ^[2]

16S rRNA gene sequencing and shotgun metagenomic sequencing are molecular methods used to profile the microbiota, each with distinct strengths and weaknesses. ^[1] 16S rRNA sequencing yields community-level measures such as alpha diversity within samples and beta diversity between groups, along with the relative abundance of bacterial genera. ^[3] Mass spectrometry-based metabolomics complements microbiome sequencing by detecting the small molecules produced by the microbiota, and its data can be combined with microbiome sequencing data in integrative analysis. ^[4]

Changes in the gut microbiota occur throughout the human lifespan, and maintaining microbial homeostasis plays a critical role in promoting healthy aging. ^[5] The gut microbiota acts as a key mediator linking aging with increased risk of chronic inflammation, cardiovascular disease, cognitive impairment, neurodegenerative disorders, and cancer. ^[5] Integrating multi-omics approaches with metagenomic sequencing, including emerging spatial and spatiotemporal technologies, is considered crucial for advancing understanding of the gut microbiome in aging. ^[5]

A 16S rRNA sequencing meta-analysis found decreased microbiota alpha diversity and a distinct beta-diversity clustering pattern in patients across the Alzheimer's disease spectrum compared with healthy controls. ^[3] In clinical diagnostics, molecular assays including 16S rRNA gene sequencing and shotgun metagenomic sequencing are increasingly used to characterize the vaginal microbiota and study conditions such as bacterial vaginosis. ^[1] Metagenomic sequencing has also been applied beyond bacteria to the human virome, though the majority of sequence data in a typical virome study remain unidentified viral dark matter. ^[6]