Lifestyle genomics · 2026
A Tri-Source Microbiome Dataset from Human Sleep Environments: Saliva, Pillow Dust, and Bedroom Air.
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Jang Jeong Yun, Lee Jaeha, Yun Hyunjun, Yang Jinho
Аннотация
INTRODUCTION: Despite the recognized importance of the human microbiome, integrated microbial exposures within sleep environments remain poorly understood. Sleep represents a prolonged period of close contact with indoor microbial reservoirs, yet the connectivity between environmental sources and the human oral microbiome has not been comprehensively examined.
METHODS: We conducted a pilot study involving eight healthy adults, collecting paired saliva, pillow dust, and bedroom air samples under standardized conditions. Microbial communities were characterized using 16S rRNA gene amplicon sequencing targeting the V3-V4 region. Sequence data were processed using Cutadapt for primer removal and DADA2 for amplicon sequence variant inference, followed by taxonomic assignment and diversity analyses.
RESULTS: Pillow dust exhibited significantly higher alpha diversity, including species richness and phylogenetic diversity, compared to saliva and air (p < 0.05). Beta diversity analysis revealed distinct clustering by sample type, with pillow dust and air showing greater similarity to each other than to saliva. At the genus level, significant correlations were observed between air and pillow dust (e.g., Brevitalea, Gardnerella) and between pillow dust and saliva (e.g., Actinomyces, Bifidobacterium). These patterns suggest potential microbial transfer pathways mediated by gravitational settling and direct physical contact. Given the small sample size, findings should be interpreted as exploratory.
CONCLUSION: This study provides a novel tri-source microbiome dataset linking human and environmental microbial communities in real-world sleep settings. The results highlight the sleep environment as a relevant exposomic factor influencing oral microbiome dynamics and support further large-scale investigations into host-environment microbial interactions.
METHODS: We conducted a pilot study involving eight healthy adults, collecting paired saliva, pillow dust, and bedroom air samples under standardized conditions. Microbial communities were characterized using 16S rRNA gene amplicon sequencing targeting the V3-V4 region. Sequence data were processed using Cutadapt for primer removal and DADA2 for amplicon sequence variant inference, followed by taxonomic assignment and diversity analyses.
RESULTS: Pillow dust exhibited significantly higher alpha diversity, including species richness and phylogenetic diversity, compared to saliva and air (p < 0.05). Beta diversity analysis revealed distinct clustering by sample type, with pillow dust and air showing greater similarity to each other than to saliva. At the genus level, significant correlations were observed between air and pillow dust (e.g., Brevitalea, Gardnerella) and between pillow dust and saliva (e.g., Actinomyces, Bifidobacterium). These patterns suggest potential microbial transfer pathways mediated by gravitational settling and direct physical contact. Given the small sample size, findings should be interpreted as exploratory.
CONCLUSION: This study provides a novel tri-source microbiome dataset linking human and environmental microbial communities in real-world sleep settings. The results highlight the sleep environment as a relevant exposomic factor influencing oral microbiome dynamics and support further large-scale investigations into host-environment microbial interactions.