Exploring the link between COVID-19 and gut microbiome at the community level

• Published in: American journal of clinical pathology Vol. 162; no. Supplement_1; pp. S128 - S129
• Main Authors: Li, B, Terwilliger, A, Maresso, A, Britton, R, Yousefipour, Z, Donnachie, E, Lyu, Z
• Format: Journal Article
• Language: English
• Published: 15.10.2024
• ISSN: 0002-9173; 1943-7722
• DOI: 10.1093/ajcp/aqae129.284

Abstract Introduction/Objective Monitoring SARS-CoV-2 virus through wastewater has been adopted as an effective surveillance approach for combating the COVID-19 pandemic. To understand the interplay between this virus and the human microbiome, this study examined the longitudinal co-dynamics between the virus and the microbiome across various sewage samples representing diverse demographic populations in Houston. Methods/Case Report We collected wastewater through manholes and the viral RNA levels of SARS-CoV-2 were monitored from December 2020 to 2021. The sampling sites serve nursing homes, rehabs, and shelters across the metro Houston area, respectively. The viral load was analyzed by the gold standard RT-qPCR method, and the microbiome load and composition by qPCR and amplicon sequencing of the 16S and ITS DNA. Results (if a Case Study enter NA) Preliminary results on the dynamics of SARS-CoV-2 viral load indicated diverse trends among sampling sites. These viral trends could be classified into three basic categories: flat, up and down, and up, suggesting disparities in COVID-19 infections among communities. A full analysis of the microbiome is underway, but a preview of the data revealed that bacteria and fungi commonly seen in the human gut predominated the wastewater samples. This paves the way to exploring the link between COVID-19 and the gut microbiome, which could shed light on the observed COVID-19 disparities. Conclusion As a proof-of-concept, our sewage bio-monitoring program was able to capture COVID-19 viral, bacterial, and fungal signals longitudinally from manholes serving a wide range of communities. Because these signals are predominantly human-associated, further analysis of these signals could offer insight on COVID-19 related disparities among these communities.