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, and it is important to find methods of prediction and treatment. As a regulator of host immunity, changes in the gut microbiota are associated with a variety of infections. However, the correlation between microbiota dysbiosis and posttransplant 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 for posttransplant BK polyomavirus infection based on the 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 the phylum level, Firmicutes/Bacteroidetes ratio significantly increased in the BK polyomavirus group. Bacteroidetes was positively correlated with CD4/CD8 ratio. In the top 20 dominant genera, Romboutsia and Roseburia exhibited a significant difference between the two groups. No significant difference was observed in microbial alpha diversity. Beta diversity revealed a significant difference between the two groups. Nine distinguishing bacterial taxa were discovered between the two groups. We established a random forest model using genus taxa to predict BK polyomavirus infectious status, which achieved the best accuracy (80.71%) with an area under the 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 in which the Firmicutes/Bacteroidetes ratio increased in the course of the viral infection. Nine distinguishing bacterial taxa might be potential biomarkers of BK polyomavirus infection. The random forest model achieved an accuracy of 80.71% in predicting the BKV infectious status, with Romboutsia and Actinomyces included.