Gut Microbiota Dysbiosis in BK Polyomavirus-Infected Renal Transplant Recipients: A Case-Control Study - Zhang, Jian; Qin, Hao; Chang, Mingyu; Yang, Yang; Lin, Jun (Frontiers in Cellular and Infection Microbiology, 2022)
Background BK polyomavirus infection results in renal allograft dysfunction that exploration of predictive method and treatment is needed. As a regulator of host immunity, changes of gut microbiota are associated with variety of infections. However, correlation between the microbiota dysbiosis and post-transplant BK polyomavirus infection was rarely studied. Thus, this study aimed to characterize the gut microbiota in BK polyomavirus-infected renal transplant recipients, in order to explore the biomarkers that might be potential therapeutic targets and establish a prediction model towards post-transplant BK polyomavirus infection based on gut microbiota. Methods We compared the gut microbial communities of 25 BK polyomavirus-infected renal transplant recipients with 23 characteristic-matched controls, applying the 16S ribosomal RNA gene amplicon sequencing technique. Results At phylum level, Firmicutes/Bacteroidetes ratio significantly increased in BK polyomavirus group. Bacteroidetes was positively correlated with CD4/CD8 ratio. In top 20 dominant genera, Romboutsia and Roseburia exhibited significant difference between two groups. No significant difference was observed in microbial alpha diversity. Beta diversity revealed a significant difference between two groups. 9 distinguishing bacterial taxa were discovered between two groups. We established random forest model using genus taxa to predict BK polyomavirus infectious status, which achieved the best accuracy (80.71%) with an area under curve of 0.82. Two genera were included in the best model, which were Romboutsia and Actinomyces. Conclusions BK polyomavirus-infected patients had gut microbiota dysbiosis that the Firmicutes/Bacteroidetes ratio increased in the course of viral infection. 9 distinguishing bacterial taxa might be potential biomarkers of BK polyomavirus infection. The random forest model achieved an accuracy of 80.71% for predicting BKV infectious status, with Romboutsia and Actinomyces included.