[Gut microbiota in renal transplant recipients, patients with chronic kidney disease and healthy subjects]

Abstract

Objective: Recent studies have shown that gut microbiota is associated with immunomodulation in transplant recipients, but the composition and function of gut microbiota in renal transplant recipients have not been understood.

Methods: We analyzed the composition and function of gut microbiota in the fecal samples from 16 renal transplant (RT) recipients by deep sequencing of the 16S rRNA V3 variable region. The gut microbiota of RT recipients was compared to that of 84 patients with chronic kidney disease (CKD) and 53 healthy subjects.

Results: The overall microbial structure of RT recipients was similar to that of CKD. The abundance of Firmicutes, Lachnospiraceae, Ruminococcaceae and Faecalibacterium was decreased and that of Bacteroidetes, Proteobacteria, Clostridiales, and Enterobacteriaceae was increased significantly in RT recipients and CKD patients compared with the healthy control subjects. Functional comparison revealed significantly enhanced carbohydrate metabolism and decreased metabolism of cofactors, vitamins, cell motility and genetic information processing in RT recipients and CKD patients. RT recipients and CKD patients also showed slight differences in that the abundance of Proteobacteria and Enterobacteriaceae and the pathways involving transport system members and carbohydrate metabolism were much greater in the former. We found that several beneficial genera in the Lachnospiraceae and Veillonellaceae were negatively correlated with such clinical markers as serum creatinine and blood urea nitrogen.

Conclusion: Our results suggested that alterations in the composition and function of gut microbiota are significantly correlated with the clinical conditions of in RT recipients, and future prospective studies of these correlations may provide evidence for predicting the clinical outcomes of RT recipients.

Keywords: 16S rRNA sequencing; chronic kidney disease; gut microbiota; renal transplantation.

1

Gut microbial diversity in CKD patients, RT recipients, and healthy controls. A: Boxplot of α-diversity (Chao1 index) of the 3 groups; B: Boxplot of β-diversity (Unweighted UniFrac distance index) of the 3 groups. ^**P < 0.01 by Wilcoxon test. The box represents the interquartile ranges (IQRs) between the first and third quartiles, and the line inside the box represents the median

2

Principal component analysis (PCA) of genus profile of gut microbiota in CKD patients, RT recipients, and healthy controls. The first 2 PCA axes are PC1 on the X-axis, indicating a variability of 16.49%; PC2 on the Y-axis indicates a variability of 11.24%. The red, blue, and green points represent samples from CKD patients, RT recipients, and healthy controls, respectively. The 5 dominant organisms that separate different groups are labeled.

3

Boxplot shows gut microbiota composition at the phylum and genus level. Only the top 5 phyla and top 10 genera are shown for clarity (with P values by Kruskal-Wallis test).

4

Composition of fecal microbiota in CKD patients, RT recipients, and healthy controls. LEfSe method was applied to identify markers that were significantly associated with CKD patients (red), RT recipients (blue), and healthy controls (green). A cladogram based on LEfSe is also shown.

5

Composition of microbial functional pathways (L2, L3) in CKD patients, RT recipients, and control groups. ^aP < 0.05 (control vs CKD); ^bP < 0.05 (control vs RT recipients); ^cP < 0.05 (CKD vs RT recipients). Only the pathways with an average relative abundance greater than 1% in all samples are shown for clarity.

6

Correlation between microbes and clinical markers. Spearman's rank correlation coefficients and corresponding P values are shown. ⁺P < 0.01; ^*P < 0.05. Only Spearman's rank correlation coefficients>0.3 are shown. HGB: Hemoglobin; ALB: Albumin; FBS: Fasting blood sugar; CHOL: Cholesterol; WBC: White blood cell count; BMI: Body mass index; BUA: Blood uric acid; DBP: Diastolic blood pressure; SBP: Systolic blood pressure; BUN: Blood urea nitrogen; SCr: Serum creatinine; TransplantNum: Number of renal transplants.

7

Classification of CKD using random forest method. The performance of the random forest model was assessed with the AUC of the ROC curve. The light gray area indicates the 95% confidence interval.