Uncovering specific taxonomic and functional alteration of gut microbiota in chronic kidney disease through 16S rRNA data

Abstract

Introduction: Chronic kidney disease (CKD) is worldwide healthcare burden with growing incidence and death rate. Emerging evidence demonstrated the compositional and functional differences of gut microbiota in patients with CKD. As such, gut microbial features can be developed as diagnostic biomarkers and potential therapeutic target for CKD.

Methods: To eliminate the outcome bias arising from factors such as geographical distribution, sequencing platform, and data analysis techniques, we conducted a comprehensive analysis of the microbial differences between patients with CKD and healthy individuals based on multiple samples worldwide. A total of 980 samples from six references across three nations were incorporated from the PubMed, Web of Science, and GMrepo databases. The obtained 16S rRNA microbiome data were subjected to DADA2 processing, QIIME2 and PICRUSt2 analyses.

Results: The gut microbiota of patients with CKD differs significantly from that of healthy controls (HC), with a substantial decrease in the microbial diversity among the CKD group. Moreover, a significantly reduced abundance of bacteria Faecalibacterium prausnitzii (F. prausnitzii) was detected in the CKD group through linear discriminant analysis effect size (LEfSe) analysis, which may be associated with the alleviating effects against CKD. Notably, we identified CKD-depleted F. prausnitzii demonstrated a significant negative correlation with three pathways based on predictive functional analysis, suggesting its potential role in regulating systemic acidbase disturbance and pro-oxidant metabolism.

Discussion: Our findings demonstrated notable alterations of gut microbiota in CKD patients. Specific gut-beneficial microbiota, especially F. prausnitzii, may be developed as a preventive and therapeutic tool for CKD clinical management.

Keywords: 16S rRNA; biomarker; chronic kidney disease; gut microbiota; probiotics.

Figure 1

Taxonomic diversity profiling of gut microbiota. (A) Overview of the workflow based on 16S rRNA microbiome data. (B) The relationship between the number of samples and the number of species is shown by the rarefaction curve. Alpha diversity comparison between CKD and HC using the (C) Chao1 index, (D) ACE indices, (E) Shannon index and (F) Simpson index. ****, P < 0.0001; **, P < 0.01; ns, P > 0.05. (G) A PCoA plot using the Bray-Curtis dissimilarity metric. The same PCoA plot with color gradients according to the abundance levels of (H) phylum Bacteroidetes and (I) phylum Firmicutes.

Figure 2

The flowchart of study selection.

Figure 3

Annotation analysis of gut microbiota species. Compositional bar plot for the relative abundance of microbiota at (A) phylum, (B) genera, and (C) species levels across samples (the top 20 in relative abundance). Interactive pie chart for the proportions of taxonomic composition of the samples shown on the left (D, G) and the proportions of the lower taxonomic lever of Firmicutes on the right (E, F, H, I). (D-F) CKD group; (G-I) HC group.

Figure 4

Crucial Bacteria and potential bacterial biomarkers related to CKD. (A) The bar graph showing the top 15 significant bacterial taxa of gut microbiome related to CKD (LDA score > 2, P < 0.05). (B) Features are ranked based on their contributions to classification accuracy (Mean Decrease Accuracy). (C)The graph summarizes the classification performance across different groups using Random Forests algorithm. (D) The diagnostic potential of gut microbiota for distinguishing CKD and HC are reflected in the ROC curve.

Figure 5

Functional characteristics and regulatory mechanism prediction of gut microbiota in CKD. (A) The LEfSe analysis defined characteristic microbial functions based on KEGG pathways for different groups (LDA score > 2.5). (B) An example of a spearman correlation between CKD-related microbial species and KEGG pathways appears in the following heatmap. (***, P < 0.001; **, P < 0.01; *, P < 0.05)

Figure 6

Possible mechanism for the impact of CKD by F. prausnitzii summarized in KO genes and KEGG pathways. Representative KO genes are depicted in pathway modules modified from KEGG pathway maps “proximal tubule bicarbonate reclamation”, “lipoic acid metabolism”, and “ubiquinone and other terpenoid-quinone biosynthesis”. Each box represents a KO gene, with red boxes representing elevation or blue boxes representing depletion based on any of the CKD group.