Alteration of the gut microbiota’s composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters
Valentin Sencio, Nicolas Benech, Cyril Robil, Lucie Deruyter, Séverine Heumel, Arnaud Machelart, Thierry Sulpice, Antonin Lamazière, Corinne Grangette, François Briand, Harry Sokol, François Trottein
Abstract
Obese patientss with nonalcoholic steatohepatitis (NASH) are particularly prone to developing severe forms of coronavirus disease 19 (COVID-19). The gut-to-lung axis is critical during viral infections of the respiratory tract, and a change in the gut microbiota’s composition might have a critical role in disease severity. Here, we investigated the consequences of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on the gut microbiota in the context of obesity and NASH. To this end, we set up a nutritional model of obesity with dyslipidemia and NASH in the golden hamster, a relevant preclinical model of COVID-19. Relative to lean non-NASH controls, obese NASH hamsters develop severe inflammation of the lungs and liver. 16S rRNA gene profiling showed that depending on the diet, SARS-CoV-2 infection induced various changes in the gut microbiota’s composition. Changes were more prominent and transient at day 4 post-infection in lean animals, alterations still persisted at day 10 in obese NASH animals. A targeted, quantitative metabolomic analysis revealed changes in the gut microbiota’s metabolic output, some of which were diet-specific and regulated over time. Our results showed that specifically diet-associated taxa are correlated with disease parameters. Correlations between infection variables and diet-associated taxa highlighted a number of potentially protective or harmful bacteria in SARS-CoV-2-infected hamsters. In particular, some taxa in obese NASH hamsters (e.g. Blautia and Peptococcus) were associated with pro-inflammatory parameters in both the lungs and the liver. These taxon profiles and their association with specific disease markers suggest that microbial patterns might influence COVID-19 outcomes.