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. 2022 May 27:12:860201.
doi: 10.3389/fcimb.2022.860201. eCollection 2022.

Gut Microbiota Dysbiosis in BK Polyomavirus-Infected Renal Transplant Recipients: A Case-Control Study

Jian Zhang  1   2 , Hao Qin  1 , Mingyu Chang  1 , Yang Yang  1 , Jun Lin  1   2
Affiliations

Gut Microbiota Dysbiosis in BK Polyomavirus-Infected Renal Transplant Recipients: A Case-Control Study

Jian Zhang et al. Front Cell Infect Microbiol. .

Abstract

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.

Keywords: BK polyomavirus; gut microbiota; infection; microbial dysbiosis; renal transplantation.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Abundance of bacterial taxa between the BKV group and the control group. (A) Top 10 dominant phyla. (B) Top 20 dominant genera. (C) The Firmicutes/Bacteroidetes ratio was significantly higher in the BKV group than that in the control group. (D) Spearman correlation analysis showed that Bacteroidetes was positively correlated with the CD4/CD8 ratio.
Figure 2
Figure 2
Alpha diversity indices between the BKV group and the control group. (A) ACE index. (B) Shannon index. (C) Chao1 index. (D) Simpson index. No significant difference was observed in microbial diversity. ACE, abundance-based coverage estimator.
Figure 3
Figure 3
Comparative analysis between the BKV group and the control group. (A) PCoA was performed based on the Bray–Curtis distance. Clustering patterns of the BKV and control groups were identified by red and blue colors, respectively. PC1 and PC2 explained 14.64% and 12.12% of total variations, respectively. (B) ANOSIM at the OTU level was conducted. The difference in microbiota in the inter-group was bigger than that in the intra-group.
Figure 4
Figure 4
LEfSe analysis between the BKV group and the control group. (A) The LEfSe analysis demonstrated a significant difference in gut microbiota between the BKV group and the control group, with a log LDA score >4.0. The increase and decrease of bacterial taxa abundance in the BKV group were represented by blue and orange colors, respectively. (B) The cladogram demonstrated relationships among those taxa.
Figure 5
Figure 5
The ROC curve of the random forest model that trades off the rate of true positives against the rate of false positives. The best accuracy of the random forest model was 80.71% with an AUC of 0.82.
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