The Respiratory Microbiome in COVID-19: Associations with Severity, Risk Factors, and Host Pathways

COVID-19 has caused unprecedented loss of life and global disruption since its emergence in 2019. Caused by the SARS CoV-2 virus, this infection shows extreme heterogeneity, ranging from completely asymptomatic to deadly. One factor that has been linked to COVID-19 severity is the microbiome of the upper respiratory tract, specifically the oropharynx. Lower relative abundance of oral commensal taxa, such as Haemophilus, Neisseria, Prevotella, and Actinomyces, and lower alpha diversity are seen in severe COVID-19 patients compared to individuals with more moderate disease. The mechanism of this association is still unknown, and it is unclear in which direction causation occurs. We propose to further examine the association between the respiratory microbiome and COVID-19 by 1) increasing specificity of these associations to the species, strain, and gene level, 2) identifying how comorbidities shape the respiratory microbiome prior to SARS CoV-2 infection, and 3) identifying host pathways that may be involved in these associations. For this first aim, we will leverage a cohort of over 200 hospitalized COVID-19 patients (previously enrolled and specimens already in hand), using deep metagenomic sequencing for taxonomic and functional annotation. The increased specificity provided by this aim will pave the way for in vitro or animal model experiments, which require species or strain level associations for proper experimental design. The second aim will focus on respiratory tract microbiome profiles in individuals with obesity, diabetes, or old age (who do not and have not had COVID-19), three conditions that are strongly associated with elevated risk of severe COVID-19. The effect that these have on the respiratory microbiome is unknown, but one still untested possibility is that the microbiome mediates some of the effects of these conditions on disease severity. By studying microbiome alterations in these diseases prior to SARS CoV-2 infection we could identify a potential high risk microbiome that precedes severe COVID- 19. Finally, the third aim pulls data from a diverse set of databases to create a knowledge graph of microbe- disease-gene associations. Using knowledge graph completion, we will predict host genes that both associate with COVID-19 severity, and interact with bacteria in the upper airway. With this data, we can propose possible host mechanisms that mediate microbiome-COVID-19 associations, allowing for in vitro follow-up to move from correlation to causation. Ultimately, this work is a bridge between existing high level associations between COVID-19 and the upper respiratory microbiome, and future work targeting specific mechanisms and causal links. Having recently published a review on all studies of the airway microbiome in COVID-19, we believe that these aims address the most critical gaps in understanding currently in the literature.