A 10-Year Longitudinal Study of Bacterial and Viral Communities in Indoor Built Environments Worldwide

Individuals living in metropolitan areas typically spend 90% of their time indoors, including commuting between their homes and workplaces by mass transit. A diverse community of microbes, including bacteria and fungi (the microbiome) as well as viruses(the virome), coexists in the air and on the surfaces within these built environments (BEs). Consequently, indoor environmental quality has significant health implications, including the potential transmission of pathogens and the exacerbation of asthma and allergies. Recent advances in culture-independent sequencing technologies for phylogenetic markers and metagenomes have greatly improved our understanding of the composition and diversity of BE microbiomes and viromes. However, most past studies have assessed specific cities and temporal snapshot samples, leaving a substantial gap in our understanding of the global spatial and temporal variations in indoor microbiomes and viromes. Therefore, comprehensive insights into these variations are essential for developing safer and healthier indoor environments for occupants worldwide. To address this knowledge gap, we propose an investigation of the spatial and temporal variations in microbiomes, viromes, and virus–host interactions in indoor air and on surfaces within mass transit systems across six global cities—Denver, Hong Kong, London, New York City, Oslo, and Stockholm—between 2017 and 2026. These transit systems present an ideal model for examining BEs because of their dynamic human interactions and diverse environmental conditions. We will use state-of-the-art metagenomic sequencing methodologies on archived and newly collected samples to assess the diversity, taxonomy, and metabolic functions of the bacteria, fungi, and viruses. Additionally, we will elucidate the mechanisms and extent of virus–host interactions associated with spatial and temporal variations and environmental conditions. By leveraging time series datasets, we will develop machine learning models to forecast future compositions of microbiomes, viromes, and virus–host interactions. Preliminary data from earlier years across the six global cities support our hypothesis that the taxonomic compositions and metabolic functions of microbiomes and viromes, and their interactions, exhibit significant biogeographic and temporal variations. The significance of this study lies in its unique time series datasets collected from highoccupancy BEs in six global cities, providing valuable spatial and temporal insights into microbiomes and viromes. Ultimately, the study findings will inform recommendationsand guidelines for improving indoor space management to enhance the health and wellbeing of building occupants.