Blood Microbiome Profile in CKD : A Pilot Study

Abstract

Background and objectives: The association between gut dysbiosis, high intestinal permeability, and endotoxemia-mediated inflammation is well established in CKD. However, changes in the circulating microbiome in patients with CKD have not been studied. In this pilot study, we compare the blood microbiome profile between patients with CKD and healthy controls using 16S ribosomal DNA sequencing.

Design, setting, participants, & measurements: Blood bacterial DNA was studied in buffy coat samples quantitatively by 16S PCR and qualitatively by 16S targeted metagenomic sequencing using a molecular pipeline specifically optimized for blood samples in a cross-sectional study comparing 20 nondiabetic patients with CKD and 20 healthy controls.

Results: There were 22 operational taxonomic units significantly different between the two groups. 16S metagenomic sequencing revealed a significant reduction in α diversity (Chao1 index) in the CKD group compared with healthy controls (127±18 versus 145±31; P=0.04). Proteobacteria phylum, Gammaproteobacteria class, and Enterobacteriaceae and Pseudomonadaceae families were more abundant in the CKD group compared with healthy controls. Median 16S ribosomal DNA levels did not significantly differ between CKD and healthy groups (117 versus 122 copies/ng DNA; P=0.38). GFR correlated inversely with the proportion of Proteobacteria (r=-0.54; P≤0.01).

Conclusions: Our pilot study demonstrates qualitative differences in the circulating microbiome profile with lower α diversity and significant taxonomic variations in the blood microbiome in patients with CKD compared with healthy controls.

Keywords: Blood microbiome; Cross-Sectional Studies; DNA, Bacterial; DNA, Ribosomal; Dysbiosis; Endotoxemia; Enterobacteriaceae; Metagenomics; Microbiota; Permeability; Pilot Projects; Polymerase Chain Reaction; Proteobacteria; Pseudomonadaceae; Renal Insufficiency, Chronic; Sequence Analysis, DNA; chronic kidney disease; glomerular filtration rate.

Graphical abstract

Figure 1.

Enrollment of study participants. GI, gastrointestinal.

Figure 2.

CKD group had a lower α diversity and similar β diversity compared to the healthy group. (A) α diversity (Chao1 index) measuring bacterial richness. (B) β diversity (Weighted UniFrac technique) showing distance between samples summarized in two dimensions, with individual points representing OTU distribution of each sample. Larger overlap indicating similar β diversity between groups.

Figure 3.

Linear discriminant analysis Effect Size (LEfSe) showing microbiome differences between groups at various taxonomic levels. (A) LEfSe analysis with linear discriminant analysis (LDA) score representing statistical and biological differences in phyla between groups. (B) LEfSe cladogram demonstrating microbiome differences at various phylogenic levels.

Figure 4.

Relative proportion of taxa at phylum levels demonstrating a higher proportion of Proteobacteria phylum in the CKD group.