Abstract
BACKGROUND: The role of the microbiome in COVID-19 outcomes remains an area of exploration. We comprehensively explored the gut microbiome of Ugandan COVID-19 patients and inferred potential implications. METHODS: Stool and demographic data were collected from 100 COVID-19 confirmed cases at the covid isolation and treatment centers in Kampala during the first and second waves of the pandemic in Uganda (2020 and 2021, respectively). 16S rRNA sequencing was performed on the DNA extracted from stool, followed by bioinformatics analysis. Machine-learning techniques were used to determine microbes that were associated with disease severity. RESULTS: We observed differences in microbial composition between COVID-19 patients and healthy controls. Pathogenic bacteria such as Klebsiella oxytoca, Salmonella enterica and Serratia marcescens had an increased presence in COVID-19 disease states, especially severe cases. Additionally, there was an increase in opportunistic pathogens like Enterococcus species, along with a decrease in beneficial microbes, such as Alphaproteobacteria, when comparing mild and severe cases. Machine-learning identified age and microbes like Ruminococcaceae, Bacilli, Enterobacteriales, porphyromonadaceae and Prevotella copri as predictive of severity. CONCLUSION: The microbiome likely plays a role in the dynamics of SARS-CoV-2 infection in Ugandan patients. The shift in abundance of specific microbes can moderately predict severity of COVID-19 in this population. CLINICAL TRIAL NUMBER: Not applicable.