Team Leaders: Minh Pham, Rays Jiang
Team Members: Andrea Vianello, Sylvia Thiong'o, Ojas Natarajan, John Parkinson, Long Dang, Jinyong Pang, Gloria Ferreira, Dylan Gallinson
GVN/USF mentors:
Rationale Humans share the air we breathe, especially in a tightly urbanized world with rapid population growth. Yet, current monitoring of airborne pathogens is limited. Hundreds of respiratory pathogen species circulate in global human populations and take a colossal death toll every day 19-22. SARS-Cov2 has reached virtually all countries and interacted with human immune systems globally, traveling mostly on human breath. The global reach of respiratory transmissions—particularly those associated with pandemics like COVID-19—highlight our poor understanding of the transmission-efficiency of respiratory microbes, be they viral, bacterial, or otherwise. BetterOur hypothesis is that better knowledge and monitoring-technologies for respiratory microbes and viruses will be critical to mitigate SARS-CoV2 (and similar airborne) infections in the future, as well as to create better epidemiological models to inform public health policy. Most current detection assays require intensive cell culture and viral titer quantification, which are time-consuming, resource-demanding techniques with limited resolution 21-23. In project DECODE, we used available air-microbiome data to develop computational methods for tracking airborne microbes.
Project goal Our specific aim was to develop novel methods to delineate and track air microbiome microbiota. These methods will be useful for future decoding of human breath. As a proof of concept, we decode air bacterial microbiomes from existing datasets to deliver the first set of proof of principle methods in breath microbiota comparison, both temporally and spatially.
We identified air microbiome composition by integrating data from different indoor microbiota captured from air in urban settings. We produced the first comprehensive breath microbiome mapping using novel time-series analyses. We recognized hotspots and dynamic patterns of pathogen emergence from microbiomes captured in different environments, and investigated associated human behaviors in those situations, for use in future monitoring and preparedness projects.
Here, we documented our current methods in 1) spatial air microbiome comparison and 2) temporal sequence analysis. Our methods will be useful in future research focusing on improved sampling of air microbiomes, which appears important and yet neglected because social interactions have been ignored in considering tested environments.
Microbial composition is distinct from indoor to outdoor air, and likely in different social settings. In this study, we examined the indoor vs. the outdoor air microbiota, in various common urban locations such as day care centers, schools and hospitals. As shown in Fig. Team5-2, we are able to consistently find differences in microbial composition and configurations in indoor vs. outdoor settings. For example, in the same hospital study, samples collected from roofs are different from lobbies, with roof air microbiota dominated by gammaproteobacteria. Similar patterns are present in daycare and preschool air microbiota, with outdoor air showing more environmental taxa, e.g., Spingomonas, than human microbiome-derived taxa. Furthermore, we are able to identify a set of signature microbial species for indoor vs. outdoor air, by examining the normalized taxa unit differences. The genus Micrococcus, is enriched in the indoor microbiota, and can be used as a signature taxon for indoor air microbiota detection and future studies.